{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Looking in indexes: https://pypi.yandex-team.ru/simple/\n",
      "Requirement already satisfied: bsuite in /home/npytincev/.local/lib/python3.8/site-packages (0.3.5)\n",
      "Requirement already satisfied: plotnine in /home/npytincev/.local/lib/python3.8/site-packages (from bsuite) (0.8.0)\n",
      "Requirement already satisfied: matplotlib in /home/npytincev/.local/lib/python3.8/site-packages (from bsuite) (3.5.1)\n",
      "Requirement already satisfied: pandas in /home/npytincev/.local/lib/python3.8/site-packages (from bsuite) (1.4.2)\n",
      "Requirement already satisfied: termcolor in /home/npytincev/.local/lib/python3.8/site-packages (from bsuite) (1.1.0)\n",
      "Requirement already satisfied: absl-py in /home/npytincev/.local/lib/python3.8/site-packages (from bsuite) (0.12.0)\n",
      "Requirement already satisfied: scikit-image in /home/npytincev/.local/lib/python3.8/site-packages (from bsuite) (0.19.2)\n",
      "Requirement already satisfied: numpy in /home/npytincev/.local/lib/python3.8/site-packages (from bsuite) (1.22.3)\n",
      "Requirement already satisfied: six in /home/npytincev/.local/lib/python3.8/site-packages (from bsuite) (1.15.0)\n",
      "Requirement already satisfied: immutabledict in /home/npytincev/.local/lib/python3.8/site-packages (from bsuite) (2.2.1)\n",
      "Requirement already satisfied: dm-env in /home/npytincev/.local/lib/python3.8/site-packages (from bsuite) (1.5)\n",
      "Requirement already satisfied: scipy in /home/npytincev/.local/lib/python3.8/site-packages (from bsuite) (1.6.0)\n",
      "Requirement already satisfied: dm-tree in /home/npytincev/.local/lib/python3.8/site-packages (from dm-env->bsuite) (0.1.6)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/npytincev/.local/lib/python3.8/site-packages (from matplotlib->bsuite) (20.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/npytincev/.local/lib/python3.8/site-packages (from matplotlib->bsuite) (2.8.2)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/npytincev/.local/lib/python3.8/site-packages (from matplotlib->bsuite) (2.4.7)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/npytincev/.local/lib/python3.8/site-packages (from matplotlib->bsuite) (4.31.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/npytincev/.local/lib/python3.8/site-packages (from matplotlib->bsuite) (8.4.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/npytincev/.local/lib/python3.8/site-packages (from matplotlib->bsuite) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/npytincev/.local/lib/python3.8/site-packages (from matplotlib->bsuite) (1.3.1)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/npytincev/.local/lib/python3.8/site-packages (from pandas->bsuite) (2022.1)\n",
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      "Requirement already satisfied: patsy>=0.5.1 in /home/npytincev/.local/lib/python3.8/site-packages (from plotnine->bsuite) (0.5.1)\n",
      "Requirement already satisfied: mizani>=0.7.3 in /home/npytincev/.local/lib/python3.8/site-packages (from plotnine->bsuite) (0.7.4)\n",
      "Requirement already satisfied: statsmodels>=0.12.1 in /home/npytincev/.local/lib/python3.8/site-packages (from plotnine->bsuite) (0.12.2)\n",
      "Requirement already satisfied: tifffile>=2019.7.26 in /home/npytincev/.local/lib/python3.8/site-packages (from scikit-image->bsuite) (2022.3.25)\n",
      "Requirement already satisfied: PyWavelets>=1.1.1 in /home/npytincev/.local/lib/python3.8/site-packages (from scikit-image->bsuite) (1.3.0)\n",
      "Requirement already satisfied: networkx>=2.2 in /home/npytincev/.local/lib/python3.8/site-packages (from scikit-image->bsuite) (2.5.1)\n",
      "Requirement already satisfied: imageio>=2.4.1 in /home/npytincev/.local/lib/python3.8/site-packages (from scikit-image->bsuite) (2.16.1)\n",
      "Requirement already satisfied: palettable in /home/npytincev/.local/lib/python3.8/site-packages (from mizani>=0.7.3->plotnine->bsuite) (3.3.0)\n",
      "Requirement already satisfied: decorator<5,>=4.3 in /usr/lib/python3/dist-packages (from networkx>=2.2->scikit-image->bsuite) (4.4.2)\n"
     ]
    }
   ],
   "source": [
    "import sys, os\n",
    "if 'google.colab' in sys.modules and not os.path.exists('.setup_complete'):\n",
    "    !wget -q https://raw.githubusercontent.com/yandexdataschool/Practical_RL/master/setup_colab.sh -O- | bash\n",
    "    !wget -q https://raw.githubusercontent.com/yandexdataschool/Practical_RL/master/week05_explore/q_learning_agent.py\n",
    "    !wget -q https://raw.githubusercontent.com/yandexdataschool/Practical_RL/master/week05_explore/replay_buffer.py\n",
    "    !wget -q https://raw.githubusercontent.com/yandexdataschool/Practical_RL/master/week05_explore/und1.mp4\n",
    "    !wget -q https://raw.githubusercontent.com/yandexdataschool/Practical_RL/master/week05_explore/und2.mp4\n",
    "\n",
    "    !pip install -q gymnasium\n",
    "    !pip install -q shimmy[bsuite]\n",
    "    !touch .setup_complete\n",
    "\n",
    "# This code creates a virtual display to draw game images on.\n",
    "# It will have no effect if your machine has a monitor.\n",
    "if type(os.environ.get(\"DISPLAY\")) is not str or len(os.environ.get(\"DISPLAY\")) == 0:\n",
    "    !bash ../xvfb start\n",
    "    os.environ['DISPLAY'] = ':1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "from abc import ABCMeta, abstractmethod, abstractproperty\n",
    "import enum\n",
    "\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=3)\n",
    "np.set_printoptions(suppress=True)\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <video alt=\"test\" controls>\n",
       "        <source src=\"und1.mp4\" type=\"video/mp4\">\n",
       "    </video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "HTML(\"\"\"\n",
    "    <video alt=\"test\" controls>\n",
    "        <source src=\"und1.mp4\" type=\"video/mp4\">\n",
    "    </video>\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <video alt=\"test\" controls>\n",
       "        <source src=\"und2.mp4\" type=\"video/mp4\">\n",
       "    </video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "HTML(\"\"\"\n",
    "    <video alt=\"test\" controls>\n",
    "        <source src=\"und2.mp4\" type=\"video/mp4\">\n",
    "    </video>\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contents\n",
    "* [1. Bernoulli Bandit](#Part-1.-Bernoulli-Bandit)\n",
    "    * [Bonus 1.1. Gittins index (5 points)](#Bonus-1.1.-Gittins-index-%285-points%29.)\n",
    "    * [HW 1.1. Nonstationary Bernoulli bandit (2 points)](#HW-1.1.-Nonstationary-Bernoulli-bandit)\n",
    "* [2. Exploration in MDP](#Part-2.-Exploration-in-MDP)\n",
    "    * [2.1 Epsilon-greedy q-learning](#2.1-Epsilon-greedy-q-learning)\n",
    "    * [2.2 Reward shaping](#2.2-Reward-shaping)\n",
    "    * [2.3 Curiosity-driven Exploration](#2.3-Curiosity-driven-Exploration)\n",
    "    * [HW 2.1 Random network distillation (3 points)](#HW-2.1:-Random-network-distillation)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1. Bernoulli Bandit\n",
    "\n",
    "We are going to implement several exploration strategies for simplest problem - bernoulli bandit.\n",
    "\n",
    "The bandit has $K$ actions. Action produce 1.0 reward $r$ with probability $0 \\le \\theta_k \\le 1$ which is unknown to agent, but fixed over time. Agent's objective is to minimize regret over fixed number $T$ of action selections:\n",
    "\n",
    "$$\\rho = T\\theta^* - \\sum_{t=1}^T \\theta_{a_t}$$\n",
    "\n",
    "Where $\\theta^* = \\max_k\\{\\theta_k\\}$ and $\\theta_{a_t}$ corresponds to the chosen action $a_t$ on each step.\n",
    "\n",
    "**Real-world analogy:**\n",
    "\n",
    "Clinical trials - we have $K$ pills and $T$ ill patient. After taking pill, patient is cured with probability $\\theta_k$. Task is to find most efficient pill.\n",
    "\n",
    "A research on clinical trials - https://arxiv.org/pdf/1507.08025.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BernoulliBandit:\n",
    "    def __init__(self, n_actions=5):\n",
    "        self._probs = np.random.random(n_actions)\n",
    "\n",
    "    @property\n",
    "    def action_count(self):\n",
    "        return len(self._probs)\n",
    "\n",
    "    def pull(self, action):\n",
    "        if np.any(np.random.random() > self._probs[action]):\n",
    "            return 0.0\n",
    "        return 1.0\n",
    "\n",
    "    def optimal_reward(self):\n",
    "        \"\"\" Used for regret calculation\n",
    "        \"\"\"\n",
    "        return np.max(self._probs)\n",
    "\n",
    "    def action_value(self, action):\n",
    "        \"\"\" Used for regret calculation\n",
    "        \"\"\"\n",
    "        return self._probs[action]\n",
    "\n",
    "    def step(self):\n",
    "        \"\"\" Used in nonstationary version\n",
    "        \"\"\"\n",
    "        pass\n",
    "\n",
    "    def reset(self):\n",
    "        \"\"\" Used in nonstationary version\n",
    "        \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AbstractAgent(metaclass=ABCMeta):\n",
    "    def init_actions(self, n_actions):\n",
    "        self._successes = np.zeros(n_actions)\n",
    "        self._failures = np.zeros(n_actions)\n",
    "        self._total_pulls = 0\n",
    "\n",
    "    @abstractmethod\n",
    "    def get_action(self):\n",
    "        \"\"\"\n",
    "        Get current best action\n",
    "        :rtype: int\n",
    "        \"\"\"\n",
    "        pass\n",
    "\n",
    "    def update(self, action, reward):\n",
    "        \"\"\"\n",
    "        Observe reward from action and update agent's internal parameters\n",
    "        :type action: int\n",
    "        :type reward: int\n",
    "        \"\"\"\n",
    "        self._total_pulls += 1\n",
    "        if reward == 1:\n",
    "            self._successes[action] += 1\n",
    "        else:\n",
    "            self._failures[action] += 1\n",
    "\n",
    "    @property\n",
    "    def name(self):\n",
    "        return self.__class__.__name__\n",
    "\n",
    "\n",
    "class RandomAgent(AbstractAgent):\n",
    "    def get_action(self):\n",
    "        return np.random.randint(0, len(self._successes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Epsilon-greedy agent\n",
    "\n",
    "**for** $t = 1,2,...$ **do**\n",
    "\n",
    "&nbsp;&nbsp; **for** $k = 1,...,K$ **do**\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; $\\hat\\theta_k \\leftarrow \\alpha_k / (\\alpha_k + \\beta_k)$\n",
    "\n",
    "&nbsp;&nbsp; **end for** \n",
    "\n",
    "&nbsp;&nbsp; $x_t \\leftarrow argmax_{k}\\hat\\theta$ with probability $1 - \\epsilon$ or random action with probability $\\epsilon$\n",
    "\n",
    "&nbsp;&nbsp; Apply $x_t$ and observe $r_t$\n",
    "\n",
    "&nbsp;&nbsp; $(\\alpha_{x_t}, \\beta_{x_t}) \\leftarrow (\\alpha_{x_t}, \\beta_{x_t}) + (r_t, 1-r_t)$\n",
    "\n",
    "**end for**\n",
    "\n",
    "Implement the algorithm above in the cell below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EpsilonGreedyAgent(AbstractAgent):\n",
    "    def __init__(self, epsilon=0.01):\n",
    "        self._epsilon = epsilon\n",
    "\n",
    "    def get_action(self):\n",
    "        if np.random.random() < self._epsilon:\n",
    "            return np.random.randint(len(self._successes))\n",
    "        else:\n",
    "            return np.argmax(self._successes / (self._successes + self._failures + 0.1))\n",
    "\n",
    "    @property\n",
    "    def name(self):\n",
    "        return self.__class__.__name__ + \"(epsilon={})\".format(self._epsilon)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UCB Agent\n",
    "Epsilon-greedy strategy have no preference for actions. It would be better to select among actions that are uncertain or have potential to be optimal. One can come up with idea of index for each action that represents optimality and uncertainty at the same time. One efficient way to do it is to use UCB1 algorithm:\n",
    "\n",
    "**for** $t = 1,2,...$ **do**\n",
    "\n",
    "&nbsp;&nbsp; **for** $k = 1,...,K$ **do**\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; $w_k \\leftarrow \\alpha_k / (\\alpha_k + \\beta_k) + \\sqrt{2\\log(t) \\ / \\ (\\alpha_k + \\beta_k)}$\n",
    "\n",
    "&nbsp;&nbsp; **end for** \n",
    "\n",
    "&nbsp;&nbsp; **end for** \n",
    " $x_t \\leftarrow argmax_{k}w$\n",
    "\n",
    "&nbsp;&nbsp; Apply $x_t$ and observe $r_t$\n",
    "\n",
    "&nbsp;&nbsp; $(\\alpha_{x_t}, \\beta_{x_t}) \\leftarrow (\\alpha_{x_t}, \\beta_{x_t}) + (r_t, 1-r_t)$\n",
    "\n",
    "**end for**\n",
    "\n",
    "__Note:__ in practice, one can multiply $\\sqrt{2\\log(t) \\ / \\ (\\alpha_k + \\beta_k)}$ by some tunable parameter to regulate agent's optimism and wilingness to abandon non-promising actions.\n",
    "\n",
    "More versions and optimality analysis - https://homes.di.unimi.it/~cesabian/Pubblicazioni/ml-02.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "class UCBAgent(AbstractAgent):\n",
    "    def get_action(self):\n",
    "        pulls = self._successes + self._failures + 0.1\n",
    "        return np.argmax(self._successes / pulls + np.sqrt(2 * np.log(self._total_pulls + 0.1) / pulls))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thompson sampling\n",
    "\n",
    "UCB1 algorithm does not take into account actual distribution of rewards. If we know the distribution - we can do much better by using Thompson sampling:\n",
    "\n",
    "**for** $t = 1,2,...$ **do**\n",
    "\n",
    "&nbsp;&nbsp; **for** $k = 1,...,K$ **do**\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Sample $\\hat\\theta_k \\sim beta(\\alpha_k, \\beta_k)$\n",
    "\n",
    "&nbsp;&nbsp; **end for** \n",
    "\n",
    "&nbsp;&nbsp; $x_t \\leftarrow argmax_{k}\\hat\\theta$\n",
    "\n",
    "&nbsp;&nbsp; Apply $x_t$ and observe $r_t$\n",
    "\n",
    "&nbsp;&nbsp; $(\\alpha_{x_t}, \\beta_{x_t}) \\leftarrow (\\alpha_{x_t}, \\beta_{x_t}) + (r_t, 1-r_t)$\n",
    "\n",
    "**end for**\n",
    " \n",
    "\n",
    "More on Thompson Sampling:\n",
    "https://web.stanford.edu/~bvr/pubs/TS_Tutorial.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ThompsonSamplingAgent(AbstractAgent):\n",
    "    def get_action(self):\n",
    "        return np.argmax(np.random.beta(self._successes + 1, self._failures + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "def get_regret(env, agents, n_steps=5000, n_trials=50):\n",
    "    scores = OrderedDict({\n",
    "        agent.name: [0.0 for step in range(n_steps)] for agent in agents\n",
    "    })\n",
    "\n",
    "    for trial in range(n_trials):\n",
    "        env.reset()\n",
    "\n",
    "        for a in agents:\n",
    "            a.init_actions(env.action_count)\n",
    "\n",
    "        for i in range(n_steps):\n",
    "            optimal_reward = env.optimal_reward()\n",
    "\n",
    "            for agent in agents:\n",
    "                action = agent.get_action()\n",
    "                reward = env.pull(action)\n",
    "                agent.update(action, reward)\n",
    "                scores[agent.name][i] += optimal_reward - env.action_value(action)\n",
    "\n",
    "            env.step()  # change bandit's state if it is unstationary\n",
    "\n",
    "    for agent in agents:\n",
    "        scores[agent.name] = np.cumsum(scores[agent.name]) / n_trials\n",
    "\n",
    "    return scores\n",
    "\n",
    "def plot_regret(agents, scores):\n",
    "    for agent in agents:\n",
    "        plt.plot(scores[agent.name])\n",
    "\n",
    "    plt.legend([agent.name for agent in agents])\n",
    "\n",
    "    plt.ylabel(\"regret\")\n",
    "    plt.xlabel(\"steps\")\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-116-13d0e3f28fba>:4: RuntimeWarning: invalid value encountered in sqrt\n",
      "  return np.argmax(self._successes / pulls + np.sqrt(2 * np.log(self._total_pulls + 0.1) / pulls))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Uncomment agents\n",
    "agents = [\n",
    "    EpsilonGreedyAgent(),\n",
    "    UCBAgent(),\n",
    "    ThompsonSamplingAgent()\n",
    "]\n",
    "\n",
    "regret = get_regret(BernoulliBandit(), agents, n_steps=10000, n_trials=10)\n",
    "plot_regret(agents, regret)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bonus 1.1. Gittins index (5 points).\n",
    "\n",
    "Bernoulli bandit problem has an optimal solution - Gittins index algorithm. Implement finite horizon version of the algorithm and demonstrate it's performance with experiments. Some articles:\n",
    "- Wikipedia article - https://en.wikipedia.org/wiki/Gittins_index\n",
    "- Different algorithms for index computation - http://www.ece.mcgill.ca/~amahaj1/projects/bandits/book/2013-bandit-computations.pdf (see \"Bernoulli\" section)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HW 1.1. Nonstationary Bernoulli bandit\n",
    "\n",
    "What if success probabilities change over time? Here is an example of such bandit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DriftingBandit(BernoulliBandit):\n",
    "    def __init__(self, n_actions=5, gamma=0.01):\n",
    "        \"\"\"\n",
    "        Idea from https://github.com/iosband/ts_tutorial\n",
    "        \"\"\"\n",
    "        super().__init__(n_actions)\n",
    "\n",
    "        self._gamma = gamma\n",
    "\n",
    "        self._successes = None\n",
    "        self._failures = None\n",
    "        self._steps = 0\n",
    "\n",
    "        self.reset()\n",
    "\n",
    "    def reset(self):\n",
    "        self._successes = np.zeros(self.action_count) + 1.0\n",
    "        self._failures = np.zeros(self.action_count) + 1.0\n",
    "        self._steps = 0\n",
    "\n",
    "    def step(self):\n",
    "        action = np.random.randint(self.action_count)\n",
    "        reward = self.pull(action)\n",
    "        self._step(action, reward)\n",
    "\n",
    "    def _step(self, action, reward):\n",
    "        self._successes = self._successes * (1 - self._gamma) + self._gamma\n",
    "        self._failures = self._failures * (1 - self._gamma) + self._gamma\n",
    "        self._steps += 1\n",
    "\n",
    "        self._successes[action] += reward\n",
    "        self._failures[action] += 1.0 - reward\n",
    "\n",
    "        self._probs = np.random.beta(self._successes, self._failures)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And a picture how it's reward probabilities change over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CgUDQwgjhXCAQCAQCgUAgSDDXPrcS5//li5buhqAR1IcVqGrb8i4WtA+EcC4QCAQCgUAgECSYzYXlKKqox9oDpThyqraluyPwSX1YweDffYInP9vd0l0RnIEI4VwgaCI1FQ2IhJSW7oZAIBAIBIIWZvqLq/Hs4r2Wdd9+cQ0u/NuSFuqRIF7KakIAgH9/ub+FeyI4ExHCuUDQBBpqw3jloZV44b6lLd0VgUAgEAgELcyaA2X4x2d7WrobgiZw/l8Xt3QXBGcwQjgXCJpAbWWopbsgEAgEAoGgBTlRWY+8hz/Cir0lLd0VgUDQxhHCeQKZP38+CCHYtWtXzLazZs1Cba0Zf3TVVVehvLy8yX1YtmwZxo4di0AggDlz5jT5eILo1NdEWroLAoFAIBAIWpCJf2ZJ326dvbaFeyJIBD2zUwEAnTOTW7gngjMRIZwnkPz8fFx44YXIz8+P2dYunC9cuBDZ2dlN7kOfPn3wyiuv4JZbbmnysQSxWfqmSBYiEAgEAoHAyr7iqpbugqCR9M5hwvk5eR1buCeCMxEhnCeI6upqrFixArNnz8Zbb71lrFcUBQ888ACGDx+OkSNH4plnnsHTTz+NoqIiTJkyBVOmTAEA5OXloaSEuUPNnDkTw4cPx/DhwzFr1iwAQEFBAYYMGYI777wTw4YNw+WXX466ujpHP/Ly8jBy5EhIkvhpTwelR6tbugsCgUAgEAhaGTuOCeG8rVJSzUIWQxG1hXsiOBMJtHQHEs7HDwPHtyb2mN1GAFf+NWqTBQsWYOrUqRg4cCByc3Oxfv16jBs3Di+++CIKCgqwadMmBAIBlJWVIScnBzNnzsSSJUvQqVMny3HWr1+Pl19+GWvXrgWlFBMnTsTkyZPRsWNH7N27F/n5+XjppZdw8803Y+7cubj11lsTe62CRkMpBSGkpbshEAgEAoHgNHL92J54b8NRy7r78je2UG8ETaG4sh77ipnhJaSIOueC048wryaI/Px8TJ8+HQAwffp0w7V90aJFuPvuuxEIMD1ITk5O1OOsWLEC06ZNQ3p6OjIyMnD99ddj+fLlAIB+/fph9OjRAIBx48ahoKCgeS7mDOL4gQpEQgpe/fVKVJY6PRHioUHEnwsEAoFAcMaxZn9pS3dBkCAq68PGckNYlMkVnH7an+U8hoW7OSgrK8PixYuxdetWEEKgKAoIIXjyyScTep7kZDMxhSzLrm7tAv8U7ijD+09vQlqHJNRWhvDaI6vxk+cv8b0/pVaNanlxLXIC6UhKaV+PVenRamR3TYMcELo8gUAgEAjsFFXUt3QXBAniX0vM2uaV9cLoIjj9iNl2ApgzZw5uu+02HDp0CAUFBSgsLES/fv2wfPlyXHbZZXjhhRcQibAHvKysDACQmZmJqipnPNKkSZMwf/581NbWoqamBvPmzcOkSZNO6/WcKVSVsZepXg6t21kd4tr/yO5Tls9z/74er/9+TWI610qoPlWPtx77CsvfFjVbBQKBQCCwM/Pzxr0faxoiImlcK+S9jWZ4Qm1ICOeC048QzhNAfn4+pk2bZll3ww03ID8/HzNmzECfPn0wcuRIjBo1Cm+++SYA4K677sLUqVONhHA6Y8eOxR133IEJEyZg4sSJmDFjBsaMGeO7L19//TV69eqFd999F3fffTeGDRvW9Atspyi2RB8NtfENwoe2ON3Y6tpZ3XP9OynaV9HCPREIBAKBoPXx9Bd7AQDj+saX2fvu19bjGzOXQVVFXHNrRSSEE7QE7cv/toVYsmSJY919991nLM+cORMzZ860bL/33ntx7733Gp/5+PH7778f999/v6V9Xl4etm3bZnx+4IEHXPtyzjnn4MiRI3H1/0ylYKtVuG6o8y+ch+oi2Ly4MNFdar1QMXkQCAQCgcCLJDm2vUtVKSSJJY5dsY9V6KmPKEhLEtPx1kiDEM4FLYCwnAvOWFIzgpbPXfO83drXLSzAuoUFAJjF/cNnNzdn1xKLqgI1JY3aVfcuELK5QCAQCARW9p80y6km+cjLElKcwl5EWM5bJdeP7SkSwglaBCGcC85YiO3ul2QJNeUNjnaRkIK17x/A2vcPAACe/+mXOLbfdPOeeM1ZxnLXfvHFrZ8Wlv4VeLI/UHUi7l1PHa9thg4JBAKBQND2+eErXxvLSQEJlwzuglvP7ePZ3s0SK9zaWycd05JQExLCueD0I4RzwRnLrtXHLZ/3byjGKw+vxFFbore66jCiMXJKL2PZHsfeKtj1EftffTx6OxcWvbwDAFB5UlQGEAgEAoGAp6zGzDMTkAj+e8c5ePy6EZ7tGyJOYa+9W87DShi14baj6M9NT8KUQZ3x5trDAICKuuhzQIEg0QjhXHDGktMj3XV9yZFqy2fVxQ2NJynVjBVTwq1QOCcsvq0pvun2snECD+rKgUezgPWvtnRPBAKBQNDM8KW2jlfGLqdWXOn0zlPauXB++8e3Y+KbE1u6G76pDyvokpmCOs2lfT6XvV0gOB0I4VxwxpKZk+K6Xg4Qy+dV7+13bcdzye2DAbRSN/AGvVRL4ycAKbb4fIEHm99i/z+4L3o7gUAgELR5+uamGcsbD5cby/+5fbxr+4+2HnOs++YzK9q1gL6tdFvsRq2ERTtOoCakYPORcmPdgk1COBecXoRwLjgjKdpXjkPbnKXQABZ7znNg40ljWfGwog85v4ex3OqszKcK2H+18VZ9Ly8DgY3Mrux/3wtbth8CgUAgaHYuHtjZdf2FZ3dyXT+kewcoKsXKfWaS1pNVDaiqF67TLYGqUuQ9/BFmLWK16n/02teQqIpdx6vQMY0ZJTZwSheB4HQghPMEMn/+fBBCsGvXrphtZ82ahdpa08p61VVXoby8vMl9mDlzJoYOHYqRI0fi0ksvxaFDh5p8zPbIvH9s8NwmycRzWyTkLeBm5jJLPG2tGnDF6U7nl6rS2O56AgBFm9j/1OyW7IVAIBAITgPVDe4Jw1KCMq4a0Q39OlkV26pKcck/v8R3/7PWsp63nBeW1eJHr6/HeX/5AsVV4t3bnFQ1sLCEWYv24v8+34MXPv0r3v7o9wCAP0/zzh0gEDQnQjhPIPn5+bjwwguRn58fs61dOF+4cCGys7Ob3IcxY8Zg3bp12LJlC2688Ub86le/avIx2zOu7tresjlCtlro598wwFgeflFPAICitC7hvCTcF3vqJgGR+IXzvJFM+19ZUo/KUpEULiYrZ7H/wbSozQQCgUDQ9pm74YixbM/S/q/vjsOSBy62rAsrKrJSnfMOhfO4m/HqOny87TiOVdTjs+3xV1lprRTXFrd0FxzUNJhzuqe+2IvutaXIiDCFSKfM5JbqluAMRwjnCaK6uhorVqzA7Nmz8dZbbxnrFUXBAw88gOHDh2PkyJF45pln8PTTT6OoqAhTpkzBlClTAAB5eXkoKWFuTjNnzsTw4cMxfPhwzJo1CwBQUFCAIUOG4M4778SwYcNw+eWXo67OKSxNmTIFaWlMMDj33HNx5MgRRxuBSb1LJvZoSd0qiq0x5WMuM1/GslbjVG3pjO2REEtKtuxJYN1/8XbpLHxecX/cwvmCWRtRsMV0vWuoiURpLQAAjLiZ/VeFi6JAIBCcKax4aAr+8K1hMdsVVzUgSXZOvfmos90nqozlVhcm1wSqQ9WxG51GDpbU4Py/LjY+n3N8p7H89HfGINlH3XqBoDkIxG7StvjbV3/DrrLYbuXxMDhnMB6a8FDUNgsWLMDUqVMxcOBA5ObmYv369Rg3bhxefPFFFBQUYNOmTQgEAigrK0NOTg5mzpyJJUuWoFMna1zS+vXr8fLLL2Pt2rWglGLixImYPHkyOnbsiL179yI/Px8vvfQSbr75ZsydOxe33nqrZ59mz56NK6+8MiHfQXvlghsHQImoqCkPYeuXTJGhRLxfhgtmbfLcptdNb6iLIDmtBROoRTSlzeLHtRXz2L843NpryhtwZJe1pJyoxeoDTeOO7fOAm15p0a4IBAKB4PTQq6M/b6knP93tun5vcRW6ZaU4hPH28NqViASVqqgJ17R0Vyys3m/NO/SnNbON5W+N7G7JxC8QnE6EWihB5OfnY/r06QCA6dOnG67tixYtwt13341AgOlBcnJyoh5nxYoVmDZtGtLT05GRkYHrr78ey5cvBwD069cPo0ePBgCMGzcOBQUFnsd5/fXXsW7dOjz44INNvLL2Tcdu6Rg3Nc+IFwestcrDIfd4MgC4aPpAy+fda1gd8fWftNI4/0a4tfMc3u6eQE/AsfN9c/l/1wHPuGfsFQgEAkH74Jy8jk0+xgtLDwBwCoztwXKuUjanqg63Lss5iRLCWLNqlWv4gUBwOmh3lvNYFu7moKysDIsXL8bWrVtBCIGiKCCE4Mknn0zoeZKTzfgXWZZd3doBphB44oknsHTpUss+AicZHdn3E0wy9VS8cB7xSPYy/XcTkNszw7KuvpZpWcP13gL9aUF1P78aqvetjXPLSv/VBwdxztX9mtCxM4wDS1q6BwKBQCBoRlKDMkb3zo7a5tyzcrDmQFnUNiu07O2f77TGmLd1y/mRKjO0srUI5+ETxdg3eTI6/OKPALSEfTYlSOEPZ2DIrp349vjeeHtdISilINGkeYEggQjLeQKYM2cObrvtNhw6dAgFBQUoLCxEv379sHz5clx22WV44YUXEIkwwa2sjA3QmZmZqKqqchxr0qRJmD9/Pmpra1FTU4N58+Zh0qRJvvuyceNG3H333Xj//ffRpUuXxFxgOyQpNYDuA7IMATuQLBvbeOFcd3FPSpEt+9sFcwAYf2VfAECPAVkJ729cqO6uWErYn9Lg1PEavPbI6kT2qPVTdgDY9wVQ+LV1fV058OkjQMhn/fqe49h/WSjFBAKBoD1DKUVDREFKUI7a7pXvT8CXtsRwXry8ssDyWW3jlvMXt7xoLJ+qPxWl5emjYTcLfe30uebpRilSlJBr27fXFQIA9pxoHYqFM5lty46ittL9d2pvCOE8AeTn52PatGmWdTfccAPy8/MxY8YM9OnTByNHjsSoUaPw5ptvAgDuuusuTJ061UgIpzN27FjccccdmDBhAiZOnIgZM2ZgzJgxvvvy4IMPorq6GjfddBNGjx6Na665pukX2A5RVYoueR2Mzw21pkCrWoRzttxjYGy3te79swEAwZQWdkgJu3tUREt0x/Pmo2tjN2pvPD0GeP16YPY3gAbuJbzm38DqZ4GNr/k7jv6Cb0LZOoFAIBC0bhoiCtYeLINKETNxWEpQRreslKht2itjupjz19qwTyV3M0O0MNOiUvauf+Sr/2Heh49E3ac2JOLPW5L9G4qx9M3dWDBrY0t35bTQ7tzaW4IlS5zuq/fdd5+xPHPmTMycOdOy/d5778W9995rfObjx++//37cf//9lvZ5eXnYtm2b8fmBBx5w7cuiRYvi6vuZxrK392DrEuZmJXMv1EDQXI5wQqyquXf7ifvS66OrLV1KzSO2PBIl0Z2AI1QNJGueEQ2ad4viM/t65MzQ6goEAsGZzMzP9xhx4smB6JZzAAhI0V2iLxrY2XV9W7ecpwfNOu91kdNfjjWiqBjwyMe4ZlQPPP0dTVEgsd8rHGLv9QuPbY19nLYeX9DG+eRFJv+UFbWupILNhbCcC84YImHFEMwBq3AeTOHd2im3zITzJM7t/bbHz3M9vimct3QptXrX1a++cxYiURLcecG7/J8R8MoNLZENak7629fNYr7nM2DBT5veL4FAIBC0CnYUVRrLig8BWnYRzm8Y28tYTpLdhfe2LhNGuDC72sjpt5x/sp0l6n1/c5GxjmjKFEl7v2/N9c6l892JrFyu2+8naB6Wv7MHhTui52ho7wjhXHDG8MK9Sy2f5YA52GZkmzHCrjHnXGk0PrM7j6TVLm15y7m7cA4AdS513e3k9rLG00sSQW7PDOT2TPfYo52hhFlyGCUCHFrJ1q2c5W9fN8v5mzcxt/gGZ44JgUAgELQ9JC452IdbiqK0ZLglE/vrDSOM5bDHvKGti4Q1EdPSebT66Gk/v6tyQ2JzNUlTqnQe7x06OnV4NwCA0ta1JG2ILYuP4P2nN7V0N1oUIZwLzlh4Ibr7gGx84/tDkZIRBNUG4dKj1agpZ5ZQidOaemXs1C3ny97a01xd9kd9hecmyUM7r7NzVRFKj5gx17k903HZD4YyhcSZkqlUCQFr/gU8lmvG7wf91bCF0gDISe7bhHAuEAgE7QL+dRhppEKed3UvKK0BpRQ9s1Mtbdq6TPin1X8ylj8/9PlpOy+lFAdOVruGJJa//Q4AoEsdS1A3rG8n12NEysqQpBldQpEW9og8Q2gPpQMTgRDOBWcsX31w0FgmhGDQxG5ISg2gsqQOn7y4FW899hU+fp7FIvUcmB3zeMEk0/27RV3bQ1GyisYY9xb/b5fl8/TfTUTeiE4IJkmNcolvkyghYMP/2HLpXvZ/2PX+9o2EgORM920ntje9b62cf3+5H09/sbeluyEQCE4zBVtLUH3qzEmEyQvNd5yf52sfu2s0r+g/VFqL19cexuBumRjSvYPhTh1u6TC5Nspraw7hkn8uRVmN05utYsECAEBuPQtNoBH3ZG80HEGSFv4Y0n6Hig8+QPWKlc3RZQEANUZupLPGuOdmaG8I4VxwxpLawWnhrDxZh2P7KrB/gzXGOD07dmksmUsq16Ku7R7Z2gFHKU//EIKK4tOfzKVFUMLO0mmqz4RwSgOQ3MF92xs3Nq1fbYC/fbILMz9vYc8RgUBw2vnouS2Y89evYzdsJyzbY84RRvfJ9rXPpz+PXhb33XWFWLGvBCer6vHEtBGQSNsVzusj9Rjx6ojYDZuJrwuYVfxwWew4dxo23+8/nsInY6YI2iznRQ/+CoUzZiSuowILioeHgu69mtUp1XV7e0MI54J2z7x/bnCNXxl3RV/fx5ADEnoN7ogRF/eK3RgtLJxzbu2U2l3R/fdr+u8nGMt7vz4BADh+wNtlvt2ghMxEcDqdBsbej1K2r5flXCAQCNoxNRVnRrUKu+utX7f2AV0yjXrnr3z/HADAm3dONLZvOVKBhoiKkmr2PQZlqc26U1eHrR58XVK7WD5vL6rAJ9uON9v5Vc21gXBR+6v2lWDexiMgaSxMrV5muYRohAnnz42choNZPYz2NBwxyuS11d+hreEpnOsJl21xHqH6CAp3tr/kcUI4TyDz588HIQS7du2K2XbWrFmorTU1eldddRXKy8ub3Ifnn38eI0aMwOjRo3HhhRdix44dTT5mW6dob7lr5sd+o9zjjNyQAxKu/fkYXDTdh5CGFhbOP3mY/f/1EVRljrNsOrbfW7gOc27rHbulIbdHhqONV7x9u0JpAIg2NKZq9e1VHy79eo1zL8u5QCAQtEPsE+b2jj052LEK7ySsdvI6paPgr1fj4kFMWD2/v/c8JEmWDHfqtkZIsSpqpvSZgrSAmbvl6qdX4J7X1zfb+T/aegwA8O76QmPdLf9Zi1+8vRm5P/wBAGB19+HMbT0SQaBzZ/T5we3WgyimW/tnO06AKmdIaF8L4iacqyo1Shzbx5rP/7sD7z+1CTXlDUa+qPaAEM4TSH5+Pi688ELk5+fHbGsXzhcuXIjs7Owm9+GWW27B1q1bsWnTJvzqV79y1Es/k5EkgtHf6G18TkoJ+N834E8onfwdJrx/8lLsupnNTiAVark1i+zJQ95JyTZ+eshYPu/6Aa5tYiWUaxeUFwLVzFMAWsIYKD4sQnoJtpQowrlIdiIQCNoZHz672Vg+E3KT2GteJzKJVZIs4e6LzmLLAanNurUXVVvnHilyChTK7o3Iabymqnr3eHIAUEHQq/4UwidOgKSk4LKhXS3bGw4cQFCrwPLB5iIUz5zpepy8hz9C3sMfJa7TZzC6EM5TV2XOv6jN8KUnMF782i7868dLmrdzpxEhnCeI6upqrFixArNnz8Zbb71lrFcUBQ888ACGDx+OkSNH4plnnsHTTz+NoqIiTJkyBVOmTAEA5OXloaSkBAAwc+ZMDB8+HMOHD8esWbMAAAUFBRgyZAjuvPNODBs2DJdffjnq6pwxwB06mIJBTU1Noyydy97eg4KtJXHv19pRVYpAsoyBE7uCSAQpGcHYO2nwNdGjcfIwE36P7i5vTBcTixxAWcTqhp+U6q2Q4DWWGR2tMfbf+P5QAGdIJk1CnPXK/cSc6wJ8tKzstaWN75dAIBC0QnjPtEiobQqT8WAXmIf1zErYsUOKipQgSy4blCWEYyTIaq2kBKwlZwNSwBDOVx8w34PltY0PhThZ1YDiKv9eCzp6jDkB8NyHj6Fm2XLIHTpgTJ+O+PqRbxjtjvzox6h96AHjc+Vip/BXUeczH43AE1VRseT1XSgvrnUNndTn1YDTcl6nhYAc3s7uKaWNKrPs+DcdthGO//nPaNgZ2608HpKHDEa33/wmapsFCxZg6tSpGDhwIHJzc7F+/XqMGzcOL774IgoKCrBp0yYEAgGUlZUhJycHM2fOxJIlS9Cpk9Wlaf369Xj55Zexdu1aUEoxceJETJ48GR07dsTevXuRn5+Pl156CTfffDPmzp2LW2+91dGX5557DjNnzkQoFMLixYvjvt6tS45g65Ij+Mnzl8S9b2uj7FiN5XNtZQiXfX8YLvv+MNf2g87tht1rnHFQfoXzon3mwHJk9yn0GtQxjt4miLxJRsz0x+W/BgB0CuxHSaQ/crp71yq3uOLb5gO6IqPFa7g3B3aX9RKXhGaKjxewbjmXXIbVc3/MyrOdCcoNgUBwxtJeJsfR4GPM/3vHeEf5s6ZiCOcB0mbd2sM2hbZEJKjavIQPC2jKK/GcJxYBAAr+erVj27i+HbH+0CnH+qASQbjwCABApua7X85ihq3OmcngTVPhNauA61i1lsq6MHTHfKW6BnJGOurD5jFeW12A287La/wFtWOoSvHvnyzBBTeejVGX9rZsKz5UhR0rilB6tBrnu3ht8hncd6wowpRbBxuf7crAcL0COb3t253b/hW0EvLz8zF9+nQAwPTp0w3X9kWLFuHuu+9GIMAm7Dk5OVGPs2LFCkybNg3p6enIyMjA9ddfj+XLlwMA+vXrh9GjRwMAxo0bh4KCAtdj/OQnP8H+/fvxt7/9DY8//nhc19HeLKN2YdIt9pyHSO6eBrJPt/a8kaay5fj+cl/7JJxDKwHV6sqVFmBKimVv7fbcLZrFw0jG0UYnClGxC96rnmEKDp7Nb8ETVQGObwMimgY/u4+zjV4n3Z5o7gxAURUofmL2BQJB64RSYP2rQHFsw4fi4pba3gir5jVeMrhrlJb+GEn24z75PeNzilb5JdiGY87D3Hv1P5f/B7IkQ6UqKKUW4Zz/LhOJXp/czgPr30TlR8wFXebOLXUwvR96Pf9v94Ny7WkDe9/z1/K7BdtR3eDtRn8moyoUlAIr3nWWWtXFDkLgGjceT04Lr4RybY12ZzmPZeFuDsrKyrB48WJs3boVhBAoigJCCJ588smEnic52XQ1lmXZ1a2dZ/r06fjRj34U1znam2VUsgnbE7/VL3p7Dxncr+V8zGV9sOnzwwCA8hMtUHqs6gQb3A6vBX8pKpgmPlo23QinAc7qYrUEyJpwrrSz+wOzr3BayuVkpzW9LopSZ+nfgaV/Bb7xKPuc5Eykh4D+7Laz74/DS7F36buXghCCJTe3n3gwAKg4WYsF/7cJl/1gKLoPyG7p7ggEzcd/vgEcXceWH3W6nY6Y3BNblx4FALz7l3X44T+jlwxr6/jNzu6X95N/BwB4WpkGgBiW8yRZQriNChu85Xx81/HYULwBAKBS1RKzb0+ulyi8IjovKtpiLMucsrzqk08A/B/bV3YXjdKKzTh6GmFzBHsW982F5bhggP9kw2cK0QTsHSuOmu1c5hFqHAqc9iKcC8t5ApgzZw5uu+02HDp0CAUFBSgsLES/fv2wfPlyXHbZZXjhhRcQiTBtWlkZm+RnZmaiqsoZmzpp0iTMnz8ftbW1qKmpwbx58zBpkv8X3d69plbqo48+wtlnnx3XtbSXG1vH/lBHi7kGAOKhbZV8CufBZNlY3r22+cqEeBKuwb9OvIflVXdaVk/IeR8A0KFTCg5uKcFz9yxGqM6q4e3Ui5UAu+DGAY5keZL2vbQ35Q0K1zgFb6XB4XmAsy/3PsaRr9j/+kr2f6DWdtg0s42cxP63Y8t5mLs3+IQ/pfWlKKlrfzks9m84iaqyemxfURS7sUDQltEFcw+S0838LfU17T8Gd+vR5ikpeg5hnm2punAeaLuW8wj3DpUlGTJh1/TmVwUW5UZBSew65I07f+y5Cu/WHjcKuz57/oHOmclurc94ognnu1azufLJw9WOhG+AMwlc1PO00RwNdoRwngDy8/Mxbdo0y7obbrgB+fn5mDFjBvr06YORI0di1KhRePPNNwEAd911F6ZOnWokhNMZO3Ys7rjjDkyYMAETJ07EjBkzMGbMGN99efbZZzFs2DCMHj0aM2fOxKuvvhrXtbQ3lzTF9qAGgrJHS4aX5dxugfcikNSyjxTVMoturb3Ksr5DkAmgQ87vgYX/Yprjgm1WgUmPK88b4dT66m7ttRUNjm1tkl0fAeEong1JadbP4TqLS5sFXdOru7UH04EH9wPXv2S20bO+17a/epw6/CSlUsuQ+87ud4x1EbvCowX5z/ID+Gx705Rnq+ftBwAc21eegB4JBG2XcP2ZFbZy92usBNgd5+c1/WBV5jj0etJfAFjd2ttqtvbKEFNW/2gU896UtPKkv1uwFX/7xAyP+M5La3wdj1KK2//7Fb7YySqpHDhZHbX9jqLKmMfkLeddHvilsVy9bFns/mgGtwabQUvUQ3fHj4CtRFRXIT4et/b62vahHGx3bu0twZIlTnfN++67z1ieOXMmZtpKMNx777249957jc98/Pj999/vKIGWl5eHbdu2GZ8feOABuPHUU0/F1Xc7fBmDsmM1UROItQVU20ApxxCevWLO/dLSdcDV+hrX9SlJTGjnXY8PbSvFwHO6GZ/1bW7fQYNmZV/8v10Ycn4PY/3Gzw4jOS2AoRf2cOzTaineBbx1i3N9jzFA0Uag32Tmmi4FTAt6wXLgTx2Bhw8DKR6ZeSs116xgCpBuU3DopdmObwG6DU/MdbQQxbXFyEnJQcCW+I63hqzcV4KpwzvjsTWPGeueWPsE/nDeH05bP6Px+Ec7AbgnEoqXhtrWo3QQCFqCSERFamYQdVXtY2Lsl8HdMpt+kOpiYzGZsO8v2eLW3jYtgb9ZwUJMp/abit/N34YiqidnU3G4LH5reVVDBMv2nMSKvSdx4C9X45J/LjW2ldWEkJOeZGnvJ/Y7qJhtpAzut7RNgb53Xl+8uvqQZZ3u1m5XnrQaZUokBISqgbToea5OF+XF5m9OKfWcK/Mx51SlIBIxPDb7jeqEor3lUc+zdsEBXPtz/wbN1oqwnAss8JbzWMnT2gL2GOlAsHmF89PF4e2lmPO3dVBVCiWiGqUmtn9lapNrK834cjmJuVrxGsg9a09YjqkPgMTlK/LyHFj13j4seT2x1RGaHXuZNJ1BV7FEcAeXArs+BDoNAqbnW2PIX7jIZUftO92xgP0PuGTuHXs7+5/ZvdHdbg1Uhapw6buXYsxrY3Dv4nst23j3y7kbjuBg5UHL9jl75pyWPsaiLsF1mHWvEoGg3ZKhJT3zGL+UsOI7L0t7Qk7EfEFyevMla99lsA27tevUR+rx2ppDWLZXK59GGqds0C3SkotQt2jHCcc6P6QoXA4e7rg5t99uaXfruX0d+9JI2NIvez9bnMc7A3/vZ7jftzRHdpuZ8//1I9OgaQ+l/fy/O4zlcEhBWVENju4pBwAcP1CBhtoIKkvrHGGZOlld0lzXtzXOvNFUEBX+QQkneBLbEtgt53xMuBuJEM5TM834u+3Lj0Zp2Xg+f3kHThysRH11GCvn7MM7f/4aFSdrsXyR+ZtZyshJMghRQVWK3J4uCctgaiwlyTks6HXP24ryIipemcO3vGMtg1a8HRh8FXDWxea6UwXO/Q58af0cdBHOtXg7NCXGrRVQEzbvqS8Lv7Rs4y0GnTKSjRjD1sb8TYl5JvXKDAPGNz1bs0DQqtE9f+xVLDSUCIUcQ/HdHpj52W7kPfyR8TkhnnIu2nBdAE2SiVXYK9oErHm+6edsZpYcNoWv3JRctkD178q/8FpS3YCfv7URtaGI4ZnlJtrTRiZaHVh+xPzA/ZRJvXpZ2oXDLu9tRbecW89t/9wivHSpubxjPvDFY55NTxf2LOwhLfSt9Kg1PCHcYH7Xy9/ag/w/rcXer9n4o3vmvPbIasx+kFWxyulh9e7t1Mt9ftvWaP+jqSAueOE8PcsjsUXBSuCxzm0iftZuOU9OC3q0ZLjIpXFz2xPnG8tfvuFduqwp8HOC4wdYcpr6Gqsm8cRBLmmNFAABBVWdg5mOPni6Wc6zOjNt5MhLejk3tjW8krKN/76ZuI3HrW55NHjh/EergHs3mNaRZiobc7qQ3G4ODV44n9gvp8VDPLz452dNfyaLDzHFGOBuyREI2iWqu9u6ElYNr7T2bEF/evE+y+eE6KpdlMX6YR0x5y9OBj55KAEnbV52nzLH2K7puvKS3ReE+H8H/vOzPZi/qQjzNxYZ34Nbdnf7u6asxrsqTWPIdpkW6DHnIcX6+1XWt4LQDj6B49wfAsv/0XJ90eiQm2L5rJfujZYjd9caa16Y1A7mD6EnfrNb3ttLUuv2O4oKGgUfc85rsCws/yeghICjG05TrxqP3XIeywU1EQJFMMlqMfzsP9vw3D2LsX9DscceTUN3abdnUl8z/wAAzepNJEiEgqoUadoAZ89cr8uNXt9RMEWOR+ndegm5x+Uj70JAdlHeuAns0QhwL6Guw4Dc/pxw3jpczBKFyr1Z+UmkotJWlQCOp6S66RO3d/+yzlCKtZfJgEDgCi88Ku6CR9G+ctRWhdF3eK6n8rc9khDFnMs42aUDe4fsP1mNvcXVeODdzU0/TwthVO6gurgRR81qbVJSG4pYsq/bE4TxnymlGPvY53H3M/msszy3dU0z50oklSnfDeHcNv7/+I3WPy9uCTJzXTwK4V2C1Y2h51vDaqpPNaCi2JrYV2RrF7RL+Jjz5W/vcW90aCX734otRlVl9aCUQrHFa6WkR7eckwTFj/Jfzd51TCj/5MVtHq0bc3x2At5VSPWITRs+uafh1q5SCjnA9u07PNfSzrCce/yukkw8zxFPNs0W55OH3dfLye6CuJvAHo1AinNdO3BrL6krQXXI6oKmcNfDu/MplFrq3LZn7FUPAKbY/PDZzag42TxlggSC08WqObswp/Sv7IOHwq2+Ooy6ypD2jmjhd0HxLm8FbIJJyBTIxXLerxNTcOw5wcbbOeuPONq0Zng383pDeNW+rDgs5yerWH6Yxz/aackVErZ5oPHJ4PxazQ9lmuFIXR5+CGnjx3u2peEwcjUBPWXYULbSsJy3oblPC2J3a9eFcvt6Lzr1znCUNH711ysd7eKpid6aEcK5wAJvBRp8bjf3RroAk9zhNPQofg5tL8X/frMKh7aVQgnHN3DaE8YNmtgN51ydF3cf4lAGNgpJE7D53yviUQbv+IFKgMiaWzs1XP3tgrYuYHtZzmVZsriU6TFDbsdq1RTv8N6WCLd2t9gIw3LedoXzKe9MwW0f32ZZF4/l/Iq8K5q3g3GSlRqn0sWD6rIGUEoRCSlGjedD20pxaFspVr23PyHnEAhaio2LT+BEeBD74GE514mmwD0tKBHgXxOBt2+L3TYBJAcSkFeDGycPqB5zLjvNPcFoKlz3Kuq0e8bDcn7xoM6ehzleaSZvverp5cayPa5bn5ac++cv8I/PPIxKHFl5tdiX1dP4nDzg7KjtaTiMFC2GQdKS61LNnV23nM+8eVTM87YoLXzPqLbz60K5326VFFb7KmlsL5/cVhHCeQKZP38+CCHYtSt29upZs2ahtta0qlx11VUoLy9PWF/mzp0LQgjWrVsXuzEHbznP6uLuhoLc/tpC63wISgqZm3fR3vK4JwolR6yWwW98fygmfMvb3amlkGX26FrCEDxqzfYf25lLCGe6v9sHMTPmPIrlnFMG8O5DLW4tiYfRt7qvV0JAnUsehXjc2oMemULbgeUcMGvX6iiasmHVvhLUcGEwEYWioKLAtW1Lc5ZmleqZzcY3RVFRVuTf0uam6d/42WHM+ft6zP7lcsv6eC1rb/xhDT55cWt8OwkEp4GjDcM8Y851IiEVp463oLeILugecJa3bQ4uHdKl6QfhhPO16hB/+1Q2T6LZRMEnDi2v1cq4uljOO6YFEZS9xZCdx9xrlUfshgVKQSnF8cp65H912Fg/qpdZ9rRPThok7R0UzIigIWC+10kMJcuhW76LZK2bJFkTzsPsd9OV0h05633ewx/hk23Hoh6z2fCyHK9sWpnlpmKvc64awrn33LH3kI6Wz3oIZzQsuZbaMEI4TyD5+fm48MILkZ+fH7OtXThfuHAhsrOzE9KPqqoqPPXUU5g4cWLc+/KWWE8NlP4wtdKYUp54tWixaij6JTk9TmtrHLz++9WoOMnibPS4V8BqyebpNagjIAUgaZZzNZbl3EM4rz7VgF2rzQQdfMhAm4rz6eBRzkwOAns/c1/vl7DHxFS3vrcSATVeVI+sLQpVsL2oArf8Zy3++MF2c71K8dXxryxtw2oY//j6H9hd1jxJEv0y8SwWzhHUXORWzdmH/D+tRWVpHapCsV/+9lAZANi1+hhKOcWeOeGITzovP1GL/RtOxrWPQHA6+KLyXuDgMs/t2V3TcGhb6WnskQv6nCRalqkmcFZnM57+7C4ZUQVL33AKjwAUvHS76V6dqwl8g7ra6qn/37Cmn7eRnKisx5FT0RUwr+541Vg2YrINy7n526QGZYeg7Qfdcn5OHhPeVEpxoMSpYB3W0xTOO6YFkaJ5fkgBigbuvU7k6MJ5+MgRJEusn4ZwbiulZv+NnrElDzxteBkAalr2vWIPfSwuqEJNeUPUkEg5aP1d/OSzOLy99Seq9oMQzhNEdXU1VqxYgdmzZ+Ott94y1iuKggceeADDhw/HyJEj8cwzz+Dpp59GUVERpkyZgilTpgAA8vLyUFLCYhdnzpyJ4cOHY/jw4Zg1axYAoKCgAEOGDMGdd96JYcOG4fLLL0ddXZ2jHwDwu9/9Dg899BBSUlxiX2Ow5DXT6u+d6Kh1C+dGzDQ1BdD0LH/Wz9oKM15p8i2DGt2HUF3zCWF8Agz+9/KynGd0TGEp2AnTLu/9ignYdmt3LMu5Hd6C6CawtDp2LGDujsc9Yv9zzvKIF7cNkwsfZGXXVBUo8fkC1l3dmyCcH685jhGvjsCyI94T5OZC8Xjhq1RFUXk9AGDXcVOwjagUozpb3fyWHlmKV3e8ihs/uLH5OuoDfTK4ubAcAIwaqrNWPYPz88/HpwWfuu5XF6lDSAmhqrTeuTEBwac1FQ2xGwkELQSl3tPFQLJslBZsUfg5STO48XZIMQW6hNQ4B4CFvzIWbxrbA5cNNWOhdUV5ws6VACb++Qtc+Df/nglmrLhuejZ/l5SgbEn0ZifoEWLXEGHHHN07GwBTBqcGnQI2v3dAlpCs1TW3C+eIIZwDMN3ak7W5pObW/of3mVI6MyWAJE5Zs73I3erf7HjNMbo3o9t91YmYlWjsj+OnL23D679fHd2wY9tpwDh/nioRt9J3bYzmM++1EMvf2YOSwurYDeOgU+8MTLp5YNQ2CxYswNSpUzFw4EDk5uZi/fr1GDduHF588UUUFBRg06ZNCAQCKCsrQ05ODmbOnIklS5agUyfrC239+vV4+eWXsXbtWlBKMXHiREyePBkdO3bE3r17kZ+fj5deegk333wz5s6di1tvtbrobtiwAYWFhbj66qvx5JNPxn2tvJu0Hj8JgD3wRGKT0DZiOacAVs9jMZ/Tfz/RV7xK5z6ZhuvM8It6xmgd5dwuL5yBE5q3HnK4IcrvIcmQoKKhNoKQJsTbhXPVSAjn73z8/m3Crf2d26NvDyQDv9gOPNnfur5kr/XzVy+yv8oiYNEf/J07AW7tO0t3AgDe2f0OLup1UaOP0xi8XNIVquCl5Qcc61VKfVmhWwJ+MhiKqMZ9v6JgFdAB+GD/B7gi7wqcPFyFd/78NbqNScXU74zBufMmoHt6dzzb53+OY/JpBqhKDQVhPGE1r/56VSOvSCBofiLUo7Qq2H0uSQRnj++CveuKoSiqEXp1WuEt5uFaICmxmeM3aQo9AAgkKHksSjhPItucKqDNWUJtQfntQa0mnEtJzPgkBaqghFiceXJQNuqXu3H9mF54e12hY/3TX7B38m4tYZ6XHobPpl9WE0KKLpzLFA1cuFosyzkApOi6BT3mPGL9rYKyZPmdbh7fQmVnI+5GO3To0TznK9kHPDsOuPY5YIxHyCCcbu0AC4OJVu3EPq/M7ZnhK+lkQ20EgawE5INoQYTlPEHk5+dj+vTpAIDp06cbru2LFi3C3XffjUCA6UFycnKiHmfFihWYNm0a0tPTkZGRgeuvvx7Ll7M4xn79+mH06NEAgHHjxqGgoMCyr6qquP/++/HPf/4zIde0c6UWM6OEgT/lAH/MtjZoAy66+kOcnBpwlA5zY8I3+wHQyoY1ATere6wybk0l5GE5BwAQGRTAvvVmOTf7oEgVCiIR3+Xk2pxw7od0Tln2jUfZ/8Or3dsechOmPL67BCSES9ImEyElsTVc/eDl1q5S1dUtLaJQVIe9laSFVc4JlxfHqo+huDZxZQj5xHV/+Xgn6irZ93nt9vsAAGdlsxwT7/z5awDA8Y11eOZ3H7K+1BxDTo8MxzH5yWE4pBiKwIObnZncvfCbtVYgaAnCNBlIct77AHt3SDJB574sSawSaiFhkhduE5yxPWwTkGW3xJ9NhVfe1lfiFxOYciEjufXZ0fy6o9fqVkzCfhsSMC3KKUHJtW65cQ6PbfM2spj7shrmbaSo1KP+ubl8sKQGyZorOrFbzqU4hHPDrV2xZJBPsimjAi2hnAKAunL39TGSOTaaKk1O2BQ9nNfLfb30qPc8gZ+jXjSdGUj9zDXbw7u09T3xTSSWhbs5KCsrw+LFi7F161YQQqAoCgghjbJcRyM52dRcy7LscGuvqqrCtm3bcPHFFwMAjh8/jmuuuQbvv/8+xkcpE8EjByTjgcjtpb2IHS+5Vm451wdkSg1LuF9Xbf2aR13Su0ldGH5RTyx90xpbmwgB9tRx7wmHl1s7AECSHW6JDrd2Sh0e3DyBoGTxrLAK521Xs+8gqw9Qcdh0Z+8+CihY7mznJrBe9y/3Yxox541/ZmRtArH6mIeyoBmJUPd+K6pisSaZ61XURrGcF1YVonemv2fs8rmXAwC2fi8xidJ4S836Q6fQtcH63Hyw/wPMGDHDsi6zwVSqupVq4RPKRUJq1OfIjYNbrEI8pdS3kkwgOB0oSGbeRTbqa8KgFCguqDRc2yNhFUke+WSbFX58TbAS0y6cB5rD1Zzv/4c/x83b5mJuv09aPPXuYx/uwBXDumFCP3Mc/GBLEaaNiW0d1mOyldr+AL7E1MGD8aGWjiQtSbYIuHYUD1dpPeZcz5avUnfh3G5RH0mZJyWRKJDE3cs+fstkSfMs1NzaaSQMhTuB3TNTbqnxu94jIVpzCec6MZJFeiV+++qDg5778Dmj+o5guWKIRGIK3629mIEfhOU8AcyZMwe33XYbDh06hIKCAhQWFqJfv35Yvnw5LrvsMrzwwguIaC4wZWUsWUFmZiaqqpyT10mTJmH+/Pmora1FTU0N5s2bh0mTJvnqR1ZWFkpKSlBQUICCggKce+65cQnmADBoYlekZyWha78OSOuguf3UckleVNVM0q6EgbIDwGvTgIbEhhIkgk2LCtGhUyo6dvPIoO1CZk4Kbv/z+ThHs6A3BXvyikQI50d2nbJ85pUIekK4CzNnG+um3DaYLUgB2LPrO9zaFRrV9X/IBT2QnGbq83gh5bRbzje9CRxe27RjdPFIqnPPMuaeNf6H7LOXVt1NON/soT023NrbphLDy61dpaqrdSOiWi3nmUFrspz0YGLdTeMhwt23KQHZoVgqqSvBK7/3jutfWeisrcoTblAsgnXhjugJaiilWPivLZZ1DbWtVPEpOLNRI6CUouRIlWEJ099Jh3eUGd5pDbXNLAhE6Z9BJLE5HEIRu+U8QcLXhLvY/67DrXG72+YCYO7S0azLzc2pmhBmrziIm1+wKoUPlcbOyn/HsDtMAVszDmRqXolBmSAoS1FjzqNtA5jlHWDCuVvbJFtd7ImEhYYRCZgWNrO6IxJ7vE3SYuUlzUi2bOfxqL/Lu+v9e4cllIhLThQg4coqg81ajq3C6POxxswRecu5PjcNJsf2coiWAb6tIITzBJCfn49p06ZZ1t1www3Iz8/HjBkz0KdPH4wcORKjRo3Cm2++CQC46667MHXqVCMhnM7YsWNxxx13YMKECZg4cSJmzJiBMWPGnLZrUVUKIhPIAcksm7X+ZbNB+SFzeeEDwOs3APsXewslLYwSUSEH47vNM3NSfMWnx0siBFi7axA/CO356gQAIF02hYHOvTWhiEiOynd2oYSq0ZPBUZVahIYWdWuf/yPgv5c3fv/+lwCpHd23pXZk8VPJmucIL5gmcUKmm6Bd45GtWBfwFz3Kkqc0gpZ84XglhPNcr1LUhGswqOMgbP3eVtw96m7L9oDP2vHNcc0hhSI7jbk0Th3ezaJlv2f1U7hu688hl3sr9I5VH/fcBrBylPxz+vVH3pYBwD3xZn11Cwk3AoGNjp3Y2DUkazWgqiguqMLbj3+NbUuPsO3d2bNywY0DDOVtQ10LKZcslvPEPkN24TxhlnMiA8kd2DvCZTwNEk0Bah8LfYRIfbb9OBbvatz7Rufe/I2u619bfch1/YFyMwfJL8f/0qxJTp2l1CRCHPWveaLFowPAxQNZgrCFW49j21HvElp/DLyM7Wl3gRh5dSjkGs6g5GLl7v7nPyP9/PONz6blnCWNPbnlC1Ru3ISAhzdcffg0KeJVxerdqiulJv7I1i6Bz4OqAoUs7Au9xrH/fS+Mukusd3nAZZ7Oz1H1uakccP5W/cd0tp5LuLULAGDJEmfmyvvuu89YnjlzJmbOnGnZfu+99+Lee+81PvPx4/fffz/uv/9+S/u8vDxs22ZmmX7ggQdi9uvLL7+M2caOqjLrqSQTRPS4sc5c7U2qAic099JqbtBf9iQw4c64z9cc6Nkf80Z2YsJ5oGV0UGeN6Wxxd02E67d9Ir/1yyOONgHJfGkbA6IkW7KkAoDikhAumlJi2zIW53VsXzm6D8huGzHnDVVAbRmQZXO/G3Y98/o4tCL2MSxxjJy3i5twfuXf3I+hC+ehamDe3cDt82Of10aLCudRLOduRFSKsBpGUGJCsF0Y91vz/PWdr8fRS38cLKlGbnoSymvDrt6M3aqje82EwmFEK66nRFTL81AXQ9COuMTnnjpeg+yu/j1+BILmQAmrOFXCntWUyDEgVIXyE+ydtmPlMYyc0ttQ+mZ0TEGSZhU9eagK3fpluR6zWeHHlQRbCu1J2RJmOVfDzLONyK5hT8NCW7BEGQrUl1s3KOGYsdJ3vbYeAFDw16sb3b2iCvcEY7++yr0m+9YSa/iRaV22llIjIJAIQbRp0SfboytCrx3TA08s3Imle05i6R5nqTCVUiQFJHwv8DnC1RLGLN1jdOVUv0HIObADABDo1s2xb/b105A8cCBqVrHcMt1PsrmW7tb+zbo1KPvB7fhx34no/OijUfvZrHz8EPD1S8DvSljZV/2+D9riShKprPrkIZYUd9j1wMCpbF2ah7FDI5bAfO51/bHiXWvyXT6MUs/Z5DZHPVlo9UJutfPROBCWc4EFPSmYHJBMYTKDK19gmVQToMdYtnjZY6etj7HQH1RZJlDCLSecT7i6H8697izjs10YbgyqPYmbyyF5JbAx8SdyTMu5qlJfSetOnag12uu02lJqr1wNPDXSOVHL7Q9c8lvgtnmxj6G/fOylSNwE0+w+7scg3CSqrnF1OFPtL9sm8p/lBxxWlVVFq/DJwU8cbT2FcI9Y9NqQAkVVDKFcF9J1vCzudp7a8JSvdn55Y+0hFJbVYf/JGq0fQEZH7yzUboTC5iRn/FV5AICeg8yJiRJRLROR8hPR3T8VFwvLwn9vxfED3pYggeB0sGNlkbGsUvYs0xAT1kqP6JmyNXdfiaD6FLPaLXtrj/VAc2cAn/usbNEUminmfNmek/j2C2ss6xJmOVcjTKiSAq7W8EEN2/BR2TcRmWMzfrRAYlBeQexV5uy3K39r+azH6us5bwhnOZcl70Rh+rlkVUFGyH0MlWLEdasqxcbfXQYAOLXXDKUiEsXRUaZVPNjFvURX6nAz9O3Kd/4PAFCv2TRphJ179Ml9Rr37vFxToTohL3ry54Sx7r/sv34/lLK4+mYVzr96kf3f/p7p7RFD4R5LYB5yQXdccOMAyzq+bLARKubym1eWWF35hXAuaNUoERUlR6oQCStQIipCPlzNeMu5kYyBf6ifO8dcHvNd0yIZ8FdH/HSQmsEEAVWljXJrTxREImbcPhJjOU/Lii1IpAZMdy1DSHDRsNvrS1JF9ZU47+Qhpvwo0upDA614MDy2mf23v5jkZPad9L8k9jEu+Bnwq4PmsXTcBFaXhEkArN9/I1+SvIA84tURjToGz+Mf7cQPXllnWXf353fjwWUPOtp6CeFuydEAIP+rw4ioESOJnd1yHvGZGK9BSVzc6H+WH8Aj86w17hVFNQSKaNQGzQzDXVPNkohnn9MVOT3SLbkYlIiKg5udVhwvvJI8blni9IoRCE4nKRmmUk3tqJWYtCnWjGGJAL0GM4Fk4ERb2dCt7wIrZzVTLzmaSTj/8RsbcLTcakFOWLZ2JcIEc8ndcp5FywEAgf2f2/aL4ZHTDArzMPeej+Vyrif81C3nkpEl09xPlrzd2vWpy1NfPoV3F/7e1RIRK+maQinStUz3lJptCQEuObtxwnNRHbv/9d4QqDhYysbwD++bhCuGdcXYPtloOF0GC/151O+HTx7SOmb7bhKpzLn8cfa/Q0+z1GyM6gixHP/kgOTLkFZTbn1fT71ruKPN4Rh5XtoCQjhvxzTURqAqFHVVYZQV1aC8WNM+UtVTy0VV5j5isZx7xaqoClC809zRjSe6A49mAacKGn8h8WLUGKaItKDlHAAkrpzG0d3lTT5eUkr0SJROWZVQiTmhMhQTNuEoEJRc65z7sZzr7u1r3zdjy05btvZN+ex+0ln1LFDuI/GKV5IUPxACpLm8yN1KqXml6eYt543M2O5lvT4dRKtz7rkPbbrlPJE8/tFO6woSxsayD3ztG1RMpUvHoHkvyAGmyOQ9WpSIir3rrKXfVEXFhs8OIWzLDH9oeykWzNrkes69X5/Akd2nXLcJBH4pPVqN5+5ZbAmx8sup46bFUiHsGQjVWecDvOU8JZ097xkdUxrb3abBj1NHNyTssA0Rl1jwRJVG1d3apYDrPOpUsLv3flFoiFI/urH868t9xnKsJHUPncOERD1R29wfaTHJnOWcEGLJeM6jH79/JfPeCLq8g2JZzvut+xI7Bw9BuFaCUm++m4lE0T2pkQYFPcs79/XqXhQZyQG8cNt45KQnO3IUNDt24btok/VzIqsrpWueBpVHgRVayO7BpVF3ieXWLsnE0zh04U1nIyXdPZgstYPTMNgeqge1G+G8PWTnSzS6nEBVavl+aHkhUHnENcO6qjK3dilAWHzz8pnAO7e7n0BVgNK9+o7ubcLay/2FyY29DN8oiorlb+9Bzal6rXsq1BaMOQeQkHPvXHUMHzyzCQBQWeIe/6WTEaww3A4BoGs/VneWCYec1jusor4mjKK95uQ/Vsx5NPhJXKIpLarGsX3l7MMaW6myzx4BZjk1pw7KDlg/y4lIt+Ey5nglOuPX11e6t4lBSwrn9nNf0PMC1/U8ETWCAGHX3diY8+YkqdMirDn1mq+2QTXZSGikZx8+5+o8ZHVOgyRbFV1umdZ3rz2B1e/tx/qPCyzrC7dH1/Av+D/3ZEwCgV/eeozVrcr/U/zVLb7+0ExmqGqZFpbPN+OAwyHFdEsmZtKmDZ+4JwtrdvgyUp89krDDhl2sxEXl0d/FvlHCzK2dSKYAxc9nbV5vZemaB0MMS2gsy3ZjmLXIjAmOlUm9Ntxg6Uey8c7VQ+2Y5dvLrd0e4y/Z3jVXDOuKaM4LV4/ojosKWNKyUGUA4TrueyRA+jn+qxjxGHXOVXavd6ktR8DWkeSA5Ci91+zY74cRN0Xf3hQaIejH8q4khHjOP0dd6l12VXapJ99/jHuYQluiXQjnKSkpKC0tFQK6DT1GQy+xBTA31NKSEqRUHADCzpeLXk5L1iecX/zR+wS89Svmw9r8v82hraXYsuQICrayjNm65dwtC+TpIt56x24s/t9OHNYm8avn7Y/aVq4qRKrEhL/R3+htxulIEtx+g3n/NCf/VKEWS7+dzFzTGlJfY9Xar3hnr715wnjrT1/hvX9oVpBTjZz01dkskD6zhceNm4UdgHUW0bhnobmtzV4u5Meqj6Gopsiybvqg6QB8COcelvMtJVvcdnFwca+LfbVrDESuhaz6vw/0torCfof+Y9kEQJaJJZ/EpkVOTw69tFRDbQTP3bPY8D7ZvLiFyu0IBHGiEqeFSgmrhrFXkoilhGCLMJ/LUN2zccKXXzYfSVBOCDViWs51pSX3vuoatoa37EpiIU0FxeVRD2sXbhONl8U7I8iqnMxcwN55EVUFIUBAUzJQ7YYJRVTNrd39+Hf89yvLZ7twLkvE03KelRrEc98diyR+PmM7T2PnZlKQvcuM+x7U4UEelAlCERUnqxrw+Q7vbPk7Bw/BzsFDECpMwHvALnwH06JvbwqNEc59ZFBvzG/Ce3t2zA7jJ89f0i6SqbaLbO29evXCkSNHcPKk/zi/M4Fwg+IoyXOiPIiM4o3oteFvwKSbHftQVYUkE0gBiVnOo4U4a3U42Y4ugoNF+9v8t5rdbUaJaDHnLqUXThfh+sQJVH6UT4QoyA4cw7d/OwE53bkBisioU6Jnz9W9Jrz41r2j8OajzPoy+5fL/XU60TQ0ckJkV0SR6FluGw2lrglLHG0adejmUXDVhiJISwqg3sP1//K5zpJ1evxgNIXBzrKdKKkrAeC0nP9r07/wo1E/wju738H5Pc5Hr8xebodAh+QOvq6hMRApDImy+6Dv8Fwc2uZRBk/jzq/+gefP+xnqjrHPRvZYmVjc6ML1zomLrvTSFaUr5+7D8It6NvkaBGcmB05WY9mek7jjguiVBRKJ6lGjQB+X+HdHmour6Wmh8qi5fHYTSm2eTtQIIAWtMefcWH/uqfctzXPLNgAEKFizAHkDR3oeNsJ5M1JKG6046dUxDQdOOkMivIStrOQsnDp5Nvae0CznKkVQkoz8Iyv2nwTAlNiEeLvHrzt0CoQTyAkoMkM1kFUV5SmZkAjxzJh/+7ksMWu9VuGIUoJAik1Z0cjcLzIaAImitoTLJ2S7hqSAhFBExTlPLAIA7HpsKlKC3nOO8jlz0eUXP29Ufwzs12M3FCgJdGtvlOU8trLIT86j/mM6Y/9GU9aTAgSXzxiGz/6zHd8M/BRA8xmKTiftQjgPBoPo1+/0vaTaCnu+Po7lr++wrJty62D02/A4ECoH4HwQdAFNlkl8Sb7c3FR5TWdzCUP86VyykSthFXKUQbG54QdtvcxMo48VYV4N0TSQ++on4YreK9GpV4Z1g801LqtzKipOWgVW3WvCC6+Yn1ZBpAEoP8xyG3z1InDLO1YhOWxzu890lk6JyQ8+Bf57hfu2a58Dhl4XWzAH0Nos51X1TDiP5/iy9jyrVEXP7FSMz+uIBZuYdf2Gsb3wmVYC52Qde4m61TWvi9ThsTWPoXt6d3x242eu59lRao5fTZlgul5DyhFIWgbheBLP1RWwffTnQZKJpeSLW4iHriDUM7NHGty/6y55HVBc0LiwB8GZw80vrEFJdQO+M7EPkgP+3iv9RnVq0jndIteoSg2luP5oduqdgYxs/xUQaioaUF8dRm7PjNiN4yFB46X9fduvUzoOltTgnLzopaN8o4RYQl3C1TmP4pE0iBwGAPQqX+fZBgDCXMLXsEKR1EgjxZDumVi25yQuG9rVYgX2cmsPqxHwTrkRhVnI9XfGsQpT0Jdj1DmXue9BohTvLGQZ/6+87h+QJeL6up13UQZSfjYNDR9+ALWGnav8QBqC6dz9QNHoGOz6EyeRTCgCySoi2lCffPIYgEFGm6SAZPFccLvGSJkZ0kSieCxGhT+ubhkfei1LXuvI1p5Iy7n2XUpB3/XT/cgTfsIqL751ME6dqDVyaMiyhLPHd8XZHw701Y+2Qrtwaxd44PIs1FY2RB34qcoeECkguZb48cRtoOPXnfsj5/YEY7csGtnaW9ByzgsTTc1oHomoGObH2paW61xnE476j3PG5NAYCeHctknadzvqEu+YoEQRNaHIBz8Dnh0PvHEjsPczp7LIXoolNdtcvm0e8MNFsTuQFmVyKwWBZJ+Ty0bGjn+4/0PrYRJkSdcT14Q5zXusY/OW84iqIoUTEDKSZcdL1u7WDgC1msKkvKHc8zz7yrkERAlWTkSqh0LSykNJLmPEixPvd90vuR/7nvRM1vaYczd0y3lVlMzw1/58NG56uHldcQXtg5Jqdh/Vc+/owrLaqIrbYCOUwzk90kGgoFNgP1TJWcpRVSlqK9ikX7d6BYKSRVkVq8TSG39YY8TFJ5QE5bWwJ1bTY82/LmhaskZKKS566yLMV0oBOUmznOvCuXff36asNNiRDmOiHp8XDt1c3BWV+srorofsuM2vXM8bCYNS816LqBQBiRjvjGtGm4rxaNnaAUDmNEJB2xxzwaYi12ztSR++BwAo/MlPzL6GCcp22d7PagSQZcjZ2Z7nd2PdF1+z9ACKee5LHrPOb2VCUFZjCsNu3gFFDz9sfgg00k7KW8sPrWb/VYW5tNsNYs3h1t7nXN+7KD4S5LlZzi+8+WzL55T0IC64wSy55rgPW0E+m0TQrMI5IWQqIWQ3IWQfIeRhl+19CCFLCCEbCSFbCCFXNWd/zjTcxrwufTuYQrvLC0BVTMt5XHW53QQO/iHJaP4EDfaHv6SwWrOct2C2du7U+iBSfKgSkVD8A4gSVhEJ+9jPbiUGLAN19/5ZltJPfP+iuRW5WS0liSApNQB6GnIKVJdHsW5uzretsPVn2xzvfftfAvQ+x3u7jks5Ol/b7DTSi2RgjlUz3BRhlc8kq2ciDnMacK/SaQCQFkizWM4jCkWAU9y4eXe4Wc51l3e/vLHzjbjax0JCkuHWTiTn/atK7HupTLb2U6UqS35FeLd25/5pWabbo67YimYV91MpQSDg0Z/dAyerMenvS/Dckn2ebWvKY0/OlbDK3k2UAl/PRllRDShkSEQxhPOhY8z7uq4qhM9mbwdgluY8fqASR3ad4g8a9Zx66JefmNS4SFACzYjNZaBnNvseHr5ycJOOW6/U41TDKfwOpaiUJKtwHsWquzQ8BABQLkcvBcb32y1z+I3Pr8KARz6O2U/dQn6wxOra7hVzXh4qA7Rx9bfzt+JwaS0Csmk575xpKmoJIYimH+At55kutc7d3NrJvt0AgPChw0gezH6jDn1ckvepEQzauAEDlkXPMp57112Wz7vTO4MQQI14j9f2MnuHy5x9V8rNEL1g9x5R++AJH4q24X/sPyu55JyTJDJbe2Pc2iMUgaCEnzzvXb7WbY6Z0z3d2Y57VzqMiIlUQrQgzSa1EEJkAM8BuBLAUADfIYQMtTX7LYB3KKVjAEwHYEvFLGgKbtYvJqhq6100THo5LSIRZqnM8mkRddNWbX0n+vYE42bppxRWLf5phhd2VZVix8oivPuXdfjoX/4SYvGcPFzl6Q5r4cCXznXcyyIjJ8U1w2Ust3aXKAiWPDBAHDXTm4OyY3GUArJPzArjz1TsIJoA7sfdetz32X+vpHExSJKscZxNEc75TLK69c0inEd5+RJCjPjBkupalNaEsPFwubFd5srj3Db0NgDulvN4FTrPbXourvbRmPuj86CoMITzerjH2z9/3s+QP+ZxyzqqWt3rJVlytQpcec8IdDuLxcyvmustNPHHEQjioUF7do9XsPt3+V5vhddRH2X5Xv/Darxw31Jg7+egH5qeI7IsQdGqFSRzVdI2fnbYWC4+bFU8GfMPn26vx/eX+2oXlQwuXClBnjb2rOcHNCG1b07Tkk7x87MyibBsWHp4TRSFRi3YD0BilAfl3drdhHN+zI6Gbtneb4s7/+vHuxxtD1aw7P5JOcyK+/qaw/hiVzFkLuY8K9V8j8qSu8u3Dp8Ezm45B9yFOXLYTBobyGHvWj2zutFGAqCEISUlQUqKnh8hdeQIy+fN3QYAEoXS4DJeb3wdqDwG+1D+o9edZf0s75AU/2EgFnhBdPwd7L8aYQYAe3a1RsbYu+JWVSCaZyFYNSUpRvUit/lnepbzu6kuM+/9rM7N6L7fgjTnbGACgH2U0gOU0hCAtwBca2tDAegZf7IAFEGQMNysOVSl5gPlolmmWjkt3YpDy4842rifzGUy/8Vj/IH9HacJRELu59jskj35dNGpVyYAIKtLKkCBdQsLAMBqWfDJh89udtRPHn91nr+dOWvtBTcMcLXSqUp0t/ZgslM4lWSJWUpPQ9mQWAm7LDTL/RZNceHDGj71L+x/Tv9GnT2kWl86xbXFHi1jw0+I6sMulvNowjlMK8iu42xCvuOYOTGXJQJF2z+kvSjdLOe6Gz2J9r1y1EXqUFWfmAnGuL45ACWQVS1J0TGW4DC1r/O+uaSvVdOvUmt2XhZz7hQEUtKC6De6MwCgtjL2hEFX5I2YLBLFCfyhu1ynJbPnq86PZ1UUqss04fC9GVC5lESSRI2Yc36s5xXffYdbJ+eGJZwXCqIIYko4AQreblpZzWB6wgwCXrHVgSYq08rqzZhjmUjA9nksZwoQte81lAnnxafKcbKqAUt2u78HwjEs536JVTKN53jNcdf1JdUNhlt7x3TzvpIJcXX51t9JMqdgSfKT0Iy7v5L69UPDXpYcjHdBB4DkrIh/669sfbcrkgxCAKrYfv+aUmDBT4A3bnRYzmtD0c9FvUoRxyLCeRPq71hV0SzntneuTyWZL1zvz+j3iRqhniGmF01nXoFu2dpzerhYzrkXcMA+L01k4rsWpDmF854AeKnoiLaO51EAtxJCjgBYCODeZuxP+2TVM8DhNdZ1W+cAx7e5xugymUUXzp0P2MnDVTiyp9y4+VVwN/6ML4BumhYx5yzbgV0eVr7maMmeGBfSdHy5fJ9mcnqk485ZF2HI+d0B+Iu7oZTikxe2omhvecy251yVh2k5v4ndEc7qm56djCK9bjh/3hgx54QQfO8v51vW1deEoUQoGuqaf0BMcXHF94Sqjc6K3ij8uLUHU4Huoxv1krz0nUvx1IanLOv4ZGnxwg8N+uQrHuFcn2ipVIWUdAKBDpuM7ZJEgDRmVXl799sATMt570zTE2fNMTZueSVjc5vojXj0MyPeNhHolvPiDGZtSRrl9M7ISs7C4ewdqAtUA2DlKHmPGFkmrl47sdx0ayqs16FbDS76ziDc88zF6DmoY8tlvRa0Wvj7ShdiFmxiWcq9Ml8DQNd+cVQ+qK+AouVj6J20CRJRDQGHFyTUiIohF7B3mz0JqWEc4MeSKEpTJREK3n1a7hAiJUxB6/WdBpoYhnLdguuMZdWu3I3yjtAt52kkjB+88jW+//LXxn3AE3YJXWoMfHjjpYO7oEdWimdbNw8pHSMUCma/JI+Yc31dgLvXkm3fyR3n5zn2C3DzUJKUhIhWwanmmGl9PetKTZnhUziPFFurQEkBBZE66+/F4ta166gscljOS6qdytm6zZvND40RzkO1wIe/MD8f07wxqcKMBfY5SUJjzvXfgvvtPL5PSikaasPMcu5iGe/arwNGXMyqtfj1juTHMofzRCKVEC1IS/vRfQfAK5TSXgCuAvAaIU7dCSHkLkLIOkLIOlEuzcZnv7VmkA7VAHN/CDx/gavl3DIQ2rRfujAfaVAMDRblb5Fe44E7vwTu3+l0d3fTpPEPyZrmj1jo1DvTdf3Z53Rt9nNHIyklYCg79OQ50Tiw8ST2bzyJef90ukLxfPeP50KSJfRI2mms65PksY8UwIi0j4yPvJeB7l6nqjHc2uGesKO+Joz9G5r/uezS1/33dUWNnN7EIH5LBcrBRrmXFdc5rSO5KS6J//zCDwO6cM71q7jKJW+BBiGm5TyiRpDe//+Q2vMtY7tMCJJ6vmrZR7ec866cT298GoC3e77e9o5hdwAA0usns75VNk44T9Jc6p6aPhoAkJsRNITzyuRSPH/uz3Cw4xaEJhbiveH/NPbLSspCVXKZYQ1hbu3mcSWZIORSMrFDJ+9JLAC88tBKy+fyYvM7l4MSMrKTfVncBWcWpVyiKd1y/vLKAgDRhfP6mvjGHV0x3zd5PaRIFVTNNZhy8wpVoQgEZSRz1tBzrzvL2AYAKOaUiFHG5LgS0MZCkhJoOWf9umZUD3z5wMXG+oCPzNIOwnXA/65F+NgWi1IybJcwOEHnaEDG1b2644Rmwa3R6tsOyAlgb3EVAOBUrXlP1IYiyHv4I/zzc9Mg0pSa7LzlPKJS5GZ4u2C7eUjp8Ard5b+aguW/mgKJRK8+c/ERcz6THLGOha+sKnC05xPIhQ6Z7u01x82xOKmD9t0+Ox6oj10ZQ6m0fnfJxHlfKeXl5geqOiznbgQ6dzY/NEY43zYX2Pup+XndbO1YCpuPOBLCNUPMuRJmyXABz2tY+/4B/Of+5agsqXe4tZ83YAOuvNsMG7Dr0zp2cw8d4UPAHKENiXTfb0GaUzg/CoCX4Hpp63h+COAdAKCUrgaQAsARuEApfZFSOp5SOr4zf0Of6exxKT/EubnE69ZexcVxSJI+kNpuETkAdOgBBGwDdCvIkOgV9jvmsj6ntyMubFtqv/W9KT5cFbPNT56/BNld0xwD4tUdn3Dfgci4qMN/8JN/XQwAkLlBUr9PThysxOEdZW57G7jFqgPuLu+JRj0UR0bfqhOJjz3Ss+D3u8i5zW+SNymYsMQsoSZcH6+k0+PD+eOV1kQXziVDh8qNMSSEzCEPY1edmWTooXMeAgAEiCacxxFnrtdXH5QzCDKRUVbNzvnOusaFqWQkB3DbuX1x7WjmwCVJ1BDOVaIABEhPSgcZeQrFmWYsrSzJGFx8LpJCbKKg2CznkuysbJHbMwOBoBxX1Tz7JGP3WuY5UHHS+7cQnHnwQmGDzWLqlqQrtydzC60odkmKFQVVs5zLJAwJEdOtnXvnKAqFqqiWyXJQc7FXdcvt2hfNgzZUAuXuz2+TcsMcXW/NtRKqBcJx5CiJgh5zfvGgzsjrlI6xfbIBwHcJOwuFa4EDX6L24wet5yAEuOhBAASoLrYIGG9lZuJwMIgZ3VhS3XqaBJUSpCBk5Av53fxtRvuhv2cC21cHzXd5Y/QIOnym94jqbv3U0YXzuqPf8dymqAp656Shd04aC4GK4uHWvca8hhTu/SRRFV07mHPQ3LoK3LznC4sbPK0357PpXc15sWWYrSgEijYCX8/27AMi1vd1Cjzeu0amfdWRRX6Mds/wZFw82dy1rg5Fv3nEUl4tJl45Bwy39tOQrV3lwgM8lOzrP2ZKksqSOsu8EwDG5O1GOld20Z4nq0ueu7dP1ApMwnIek68BnE0I6UcISQJL+Pa+rc1hAJcCACFkCJhwLkzjfnnzJuc6Tkh200haXN1tArX+gs3tme60nAdtcR924byZajDHg5cXm31AaAl4xUcscl1ibDyxCXoSUYFHXOK+dE2u0Z4TzuKIRyMurnzdB2ShS14cVu1Goq581n/jeXe7v4xuegX4WfzJ+AAASWnAoxXA+T9zbvObrf3wKqBgeVyn9SprZo9BjwdeONctI3wMZFi7TxQXpRvv1h7msroTze17e/UHxroeGT30nQCwa7lrpDX7bSwWH14MhSpI7rQEAPClR4xlLBrCimE9BwBJUk3hXMvM3iGpA4Ky1TVTIhKqk1mOCEIlbCnegupwFWZvZRM6t1CQ0qPVcffPLd4O8M6lITgzUVwUazp6mS8eXkkfT0Z0RYs5lxCBlJHrajnP6JgMVaGQuWegYAubwhkuqrtNjy38dyowa7jr+fjqBnHz0iXA/7SURmdNYRP0ja83/ngc+vioZwav0ZKyZqY0svwVgAtla3qlsERYOTVQ4B9nA0tMJfubHdi7tSCJjUthBFCPJBDOEHOsIvr8Ii3JfD+t3l9qSQgaC14BFFYo+OHOHsuuh0NRxVl2jy+/aa4jcLsl9XW8sM1bzoNKGB//zFSS/3H1bHx/x8foV3HM9RqC6RHISSrkZNv7TFWAFy8GPnIvmwkAWdOmWT57Cud1Wh6hcJ0j5MEtFIuGQsa87Pijf0TFe+/h5KynHO088XphUIVtiyNUIm70eYESgjGXDNcCuxZ67lJVWs/mpxx2T0w9vvwbdwzB9Q+Ow8W3DIIbUZOnipjz6FBKIwB+CuBTADvBsrJvJ4T8iRByjdbslwDuJIRsBpAP4A6aqOK9ZxqPZgGr/2URSLYscWqoo8Wc64leRl3a2yz9Ez4bq6tuw/aK861ucY4Hv2WF87XvH8Chre6Zat1qGLdq4umu24AbdL4Yjd9L+80zOppuXqE6/7+da61zWTot2dodXhzRKNrg7t7UeTDQsW/TOtJ7gnNdPKXU4uSzQy4eMvCO1faDRUenfajhLU2a615RtTNHJ58QbkeR6fL3yNWsbE21YgrPKbJ2n2nnI4RgeK775NyLsO0et9cd9ktNSLFMmmSL5ZwdM0ACxrXpBEgAO7oyF/SAGgShElSiYtaGWQASVwKtQ671uR0wnlnK3DISC85ceAHb7sZe61Kik1e+Up/lUU9FeuJww1gAgEQUSBKMkle85bz34I6ORKKDzu1uHkgXWHRK93qe083Tr1FUuSclayz6dxy0CQSNeiw9XG4jINbQKD12HkDIJsCUogPqEcSmg+Z11sUozaorGFbtK8F3XlqDP7y/PWr7o+V1huDNu7WHFRUBzmX7V3M2W/YzBG+XdzVfflNHIu4KI115XNDBvJcGVJjehyNKD1gUDv0r2Xsq2eP7pZRATqFI62ITrH0YlYLdulk+JxMPIXexlgBZDUOy3RyFZS5Ks/oGSKm2uVo8N5WbcE4pZzm3Z2tvhNC6+jlg+Uznet3IY5Tt1fr9ltNjgqfsuE2JZHtW+4/pgum/n4BB53ZH9/5ZCCS5z6ukuig2XGE5jw2ldCGldCCltD+l9Alt3e8ppe9ryzsopRdQSkdRSkdTSt1noW2Vmvjq+DaZT39tuTGNzKscahS3dv3lKMmSMSF8/9Sj2FBzPb6s/DH+98gqM2mL/aUby1V3wGVxXEj8rFtYgK2c67juyge0Dsv5JbcP8d3289lxJPoK+XTd04VHTYnCvwP2fO1/MuPm0iYHiCVpTHPBZw/2hZvlPBFJglJcXK0aWbvcD/ZEcDpNcWvndaD65JO3aKj6sstcgRCCJJlZub46dMJYf8XQbo62WclZAIDOaZ2RLCfj5+N+jnolthcJ378fj/oxvyWu7ME6xZXsnC8sPWCsI5KKlAhzVVcJG79uHnQz3t3zrmXfUw2nkBpiv/n4wisRUINQJHOcjVUexi/2+LoB45hwrjY2k6+gXcJby+2mjAFdMmCHF3q9kq6F6q3v7zdLnsWXley5kxGBzJXgLjhgjnWLXtmJ3WuPWyxZmTnMq46qFOAEqtqTSagp1qzjO+xOlJwbfFM5uTN2mzg4cJJ5wfCx/o0mtaPr6hDRLec23NaBoAFJSIY5Bh0oqUFxVT3e/vqwS3tzjH/gXSZMv7nWvR3ABP0L/roYD7/HPMx4K3soolpkvvmbrMpb09PK+uK4cng3V8u5l1u7vupEmvl9fevgKmP5ka/+ZyhLnr1ljLFe9Yj1pipAUzuDSLZz8WOrz3E2Cx5zLs7N3E8+gqpPP4Va4zyWUlVliZf3xE2Qz/8OcHQdsH+xS8x5I+7fT38DfPFH53p9vq97b7gZhPxw0FljPreHcwyzIz83xnujiDkXRGXv58CT/S0a0NNCDO0Ym/TqWVRt8WraICzJBKTYqVkN1ytY8Y6m+T5ucw2OpoHM6Q8kN7/bM0/pUXPQS5RlqymkZnhnMfXCK457wrf6mR/Ws8Rbg1KWRD+YrpXXfqdB55qCFN+3/mO7RD+Ml+X8NJRSUzUrJ771NPB7H7FZ1S5Kh+ZyzPGbEC7efmx7D2G9vI6NkBKCSlVPt/eop+eWdSsFn6E9pI0jbmXOCAj+/CETcolkKgDdhO6huUMBACmBFKy7dR2m5k31VQKuLmJaGtItITVqXC6ZOm7ZcgEVl+y7lS1pngJpwTRH//J35SM1zCYM/cpGQqKy0R6IPr7E89vYrQR6fgflNHilCNoOShTLuVupQV44d6vgUnyoEi/9fBn2rXd/LiUSgSQTQ3apr3d7B5jriJ6vRqU4NecJnAgPAAAc+qITDi/WUgqd3A0AKD9h5lNImOUcAHpPBHpEmcDHwXwtE/6a/ayUp64ASU+K36290kOZXg7VXRAPpGBIg3PsqqdBpBDr+glPfIGH5m51Pb4eN+9HwaCXYHtvA7vu5XtNI1NDRIUsEXTwcOk3LefWsWxc346G5dzu1k6pc5zU7+uAh0dmihIGamux54ILMemQmTTuni3z3S+KAjSiugjn3HzZp/D6XNLT6H2xs6wrfw1yY4P8KcWecyZg/xVTodbFyBFx0qUC0h4z3wts4VnY/wVQ4bM0ciwMy7nWx5Rsz6ZxVYnwiUSiyDkJyunT0gjhvLko1JJXrXv59J63vtxYHHSOM5uz5eXMDZIr5+7DliXswZVlAmndC66H37VKi+lJsmm3orm1B1ISn5wrHlrB3Lax1vuYMYKaW9GlWc/gnq4uOQh0iNVy3qlXJq65bzQAIDM31QhZcKspaTmMi7ZWkkliJ1YeGGX9qOLPjdxNqO3sHsMUN8NvAC7gYs/jdWv3K7R9/gd4vWpCagjnvH4Orn//+vjODVtCOG3SzU+adFdy++/dObUzfj3x1/hk20ltuykM8AI1AHRK7ex6vyS5WoOsPLD0AWM5WebyWxDVUjZoaeFSPLb6sZjHy9CSVP3mqsHGuqIKc6KsEu/fY1TnUVAl9ivIakBza+esP82k/NPDcRJmURS0CywFV2zjyAmXSga8tdxtnD6pJSAt3Omu8JRhFc7d4IVz3btKVSje3HYn5pQ+6dxBy1kT5tyxG11KzW0srSlhib4SEG53/VhW5ukHFzKl+N9vHIn//WACeue4Z5L2YvPJzbhg+U+xKM1pZXygfg9LtmsnkIKjLonn6pHkHfvsgi7supUtswvG0ZzL9hVXY8uRCkw620zM/Obaw/jTB8zbT3+HUJtSNyARI5Eon8dEd/+2T3Mi2r0wpIv3fKRhzx4opaUoetBMrte36oRrW0qSQBXV6eD2ycPmchxhYuldG9B9wikMfOIKZN90IzvHni+M7Y0Wzrn3Ze1XMRLgrnku+na3snb50d3OfaMbAeu0MaPKGf6mE7GHXCTAQCIhynMtLOeCqOgP2a4Pm+f4Hi+dT/7xMT489Qhr4lLbkhnOdcu5OQpv+vwwdixnD5gUrgKB+whtZFTNtLmwRnsJysEWFc6T46mP3Uw0Ju493KDg3z92WsSP8TXKNbciQijkaNpEm1s7AMhB1qelb+7G7F8u15rF309ZJnEllWsshuU8WmZVnhqbdnvETU5tcmO58b/AZX8yP8crnNc6Ne+uEKDEIytwSAkhpIawr3xffOeGdTKkuFjOwx4eOItvXowr8q6A8eqQvIXzEi0uTFUpNheWG+uTpNjC+fKjZtK8lEAKHhivCetEwZDupib+p4t/inf2vIPCqugZ3PWMw92yuIkxJ2DrlnEAXCZ6xrcHfRsRTjhnlnN2PEVVoienaQK65fx0KL4EbQfeWu4mbNlRFYpAkve9pOccibjUygY0y7kU/T6ULNUL2HJVaZTwFW2ssSSra6yHyNf/ca4r28/+l7hYFxtJSpB9h+nJAVw0MP6qQdtLmDfi2lSPEotuSsuaYlxUZ36P+1UWgx2BjAz4z76vhwK5ZZh/ngv1AYDCU9GrQ1TVRywe1b+ZtxX/XXkQAOfWTt29gCQiWWLO9aHT7gGiZ6E/p3fTra5yWgAUAVCFOi3nRVzp2ShCXdff/87ymRAg+6w6yN36ISkvj62kfD4T5zzKbmhJHjgQaePHW9ZVL19mLNOmhjO5zUnqyht3rEqb8B3LOn1kPcuDdWyLxYuV7cuNM6k5jepO1NB8EXMuiE4zu1J7DCT76y/AoYbxWhMVycSaNdiard39AZMaSh1ZFR002Mp92d3aq7mEDdXFrNTJaWTiNWfhh/+YhOm/n+CZVOJ0Yrecj5jSq9HHGnkJV6EwlvZUx0i/z7vjsnW8a2GsOuduMLf25hcgqG45t3tteJFsazf1b4ntEE+8Meern/F5XO8hmo85jzf+nJ8onNAy/Ua4l2Y4lvZZT/jDKYRONZxybfri8gO49rmVRmmfaLVw3UiWk1HRwBLPBTJ2Y7LLxLgyFL1ere4KL9VGoKoUJXUlkFNMFz+VKHj/OhYHaxfOVapC0a5TpkFInOU8QiMWhVauzYUvOc2fMmjaA2Md6/RY9kZbFAXtEj6sQ6VATUP0ibJeixxw98Rat5AJVnvWulsdJSiQT26BEkX/bqk7rL1DvEqjqREYc4/KElPAbNQ7JNIA7HTGrxtEUV5QSrH3lHeCOr4d4B7iEw8xy0i6WToBdNVKeeVGFMgSsOSBizFCKsAFcvSkbjyKJuh9a1R3x7a/fbLL8rmOU9K4heWM6ZPtSHimtzUUvLaEcHppMZnImL1tNv7x9T8AcF4WtvP88t1NAICAh5GoNiUdfl0iiSxBqSdQq6qivU6Bj37puSnnllvQ/+oTGHCNLVROjRhCMO9x4Cach23CNlUUkCTrbx4pMrPNyxkx5jm8556ds6a4S7CNdfkusT0njuPYzvUG8ybAC5OMVaPT5mN67s8ANYx7ut6EO7vcApw1uXH9iYawnAuiEnUUSAD6w9HnPM8mkbCCLPkYugfNBGOqSk2NmseEXl7+N5BobiMAcFIb0C/5LZDWyfmw/mOAuVxV5N9SGIOje07h+Xu/RH119AcwJSOIlIygr+QSpwO7RdrrNe8nRrWrR+3HGB1g/zkBzF7GgjVrhHAeOD1u7XppH1z9T387NNjKWdnL/yWSeC3nvl8g1t+jb6aZb4C3VI97fRwqGipwvCb+TMXFVUw4D3HWcr2Umvf9yPpFOOH8waUPurb8fAeb9Otlni7ufbHtSNHvuWQ5GbvK2HiTlLPMiJ/kOVp1FPtOeXsQfLn7JNJV4NCr+7Bm3n5cM+8ayCn8d0XRL4t9t5f1tSavDKthQxiXqGSxnIeVsOWZUc6zhhINvcA5GXajx4BsxzrZcGsXlnOBCV93WlGpo5wVwMplfbyVTfTViMpZzp1t/eRXlKAYgn2GdNJIVmhsd3FrVzwziBMgjVnMPvuPKWA2yvtqwU+Ag8us6zpwim+uJJmdTws+xfXvX48PD0T3btSHQI9cY40iU/sdFhZyFkk3r660XFYDHcwwm5Uso18n09V7DImtXABMy/ngbmzuMKirNQcQP86nBs132W/mbYOdC/p3ci1nuXhXMXYeK9c+SRjRM8vYpsuJetz5qzte1T67C+dfFzBFb9ayz12vp9f0KCF8NiJVIdSdYMcP10R5T++YH/U4SZkKgmm2e1RVQXTFlH4Jfc63COcPXD4QAJBvT8IXiQBBl99c+06I2zaeaOFhvc5xXx/F/TwqDZryu64cWPgr87NOdh+ubZXp7g4gJT2IoRf2wAUdXkVu8DBQtBEyiSBJqgM69GxcfwDIaMDYK/o4N4iYc0FU3JJRJRLddeNs7yzoSkSFTMLonmRmL6UqNa18HsK5VH3E063dQTCNCSaqAoTrmSuLlqTMYOBUlhQuAWz6/DCUsIoi3rXbRu8hHTH4XGfm6JbEbjl3S8wDAAc3x87wb8kOPfib/jqgWyu5gctNEI+nbFP/sV0w9a7hpz8hnEfGWwchm3DuI9a50fi1BueerbX3KcyfOoh+Ifasz0Z37C0wvSbKG8otTWdvnY3L5lyGlRtejHlYfjKUnca+l7BFOGfn5CdtT07mYkd1y4gU+0W4/hCbaOm3lp7B3ThUFAvIxb0vBiEEsv59EdVhgQCAXy79Jaa9P82xXiclKGF4iP1GGz8/jKpwlWfbb55lfaYiagRUE8aZcC4ZnyNqxPrMJFuf86a4vOv7Csu5gMfu1u7m2v6dl9bgR29sAFUpKDWTDbopUbO7RM+0rEKGhAjLeE0BgBjCvo5FONeWw7xF/xZWAaG48xj8u+Q91FQ5BfdGKXj3feFc94NPzOW93gWAdpQyo8Wvl/866in0r7uplnN9fwqgSpaQFwqjd4T7jtySdWX1RljbLwI45mUDpKPOfVzQ7xnd68KeId0ryWD+V86s7rJE0N+lKsDOE8fxwu5H2Qcq4YN7L8TN45miRP/m7GUqdQu85Z5uaMBNexYjI1SLtB3WUm06NNw462gkZzxwzbON2tcVNQLImuVcd2snxFA6AECKpux49IMdRtUQgLmtS0kucxJdGROJ8W7VM6X/wsWDItr8ojEx37qn7NK/AV+94HyuhnzL+3R15ZCD3HhRuNZcbowgrRmY7uk2HedNG+DcLizngqis+2/zHr9EsxJtfN2ziRJWIJMwJC6lFKUwrfrai61gi1UglEgktlu7TmY3JuxTxSyv9sF95vbz72UCfIJq9craQGep3WoTdKfcNqRVuLLz2AVhr/GxtiJ2UhJLAqqeTndY9w5Ys7W79Qnw5/Dxvb+cjxsfGo+pdw1H/7FdfCeEK9xZijXnXImdg4fEzkSqwQuHhlu7R9x45ZEUHPysk/nd7vnU2iBR8eZu+HRrf3vsNCzISAf6nIeQEkK1XYHgQkATXiccXItQ6SSESi9CUAo6XLlf3s6ST/5is3vpNR7+kXnyU5Y5OWzJ1s5ecHqCn9+d+ztMzZvKtkVUmJZz/y/CAyd9lv0D0DWtKwAW7w3wkzoVy/fEX6Kyb24a+kZsNzd1H5PsLv05KTnY0v1LAMDuzl9BpgHDkm6vwd6YCfw9z13sul63nEcamp7UStB+iNiE83vzNxqfs1KtY1yN9j6pOMnGW7dxuu9wZ+JYHpUGIGn3u4oAVEiQFGtcMv8+NtzaQ+Z4oqjMa2nbsBkAgMOFzrhrr5j3uMnuDfS/VOu89zHtOTK80JWHjc3xpaOHy+i/QIHNpRl7PoGDQAoi2nkVQiDZFJmqSz1xNx7/aCd+8sYG496xe0Tx91SsUpWyRDCqV7ZjfSndZCxTrV/6afTpn2RzPzDc2rnpZulL/8EPdizElQVr3DtACGg4DBr2J9ildDfvtbrN24FhnBJ3wt2+juFACgAgAFVADOFc23ZoJXof/xwrk+9FABGj5Btger0cvPEmhAsL3a3j2pcRUzhXwkByFpDlEiIZbT7SmBBTXTj3Eqa5RNT2jIIKlSDzCVcXPcptbIQgHWsfEXMuaFGStEyhk7zjZJRQGDIJY1ja50hJZ4MAVanpn7XmOeDAUhRss7qcS4iAxIrnebiQnXvY9WygUj0yaJ8qYBbLZtRm2S0HCdIDJJTUTKuG1CuRTzgUWylisZzbrYgXe1gBDMs5J5y7zDaiJvHRyOiYYimP4Tch3PtPbcb6ccz1uWH3bmeDE9sd1Q2oZSKq3V8eVuqiVR1RX5YEqmjXVWh7uTfnjeHTEv74vrfw2865QNUxfP+T7+O8fO+wFP272Mtp16mSiYbiq5CZlGnEYdupkyTgmfHAsS2u2wH3+4+3nL+7jX1320qZW2NpnTlGNEQU6K+OYLb/F/1TX7i7YGYEnVaY0V1Go0d6D1zY80IAQEWtdn8RFTuORY8vd0NR4ch6373qLGO5OMNMKNeng9VV7tI+l+L3U1iSzcEnz0WPyrPRvYp5AkXUiEXRRggw4uJeuOpHI3z167IfDjWSJdnRLeeLXkls3WZB24a3Mn518BRW7TefTXtM+YFNTJGlj6Mxq3+40D1ph6HgV6kMlcqQtr5taROqM58uXcEbqTWF31Ctdcx2S1brFaMeFc591now7T0WpcTrsE7DfJ3CsJw38f3BW87t5KgU6DQQ61L46uUAkjMQ1s4bgoQvhv0ZADBHuQgA0AD/CuePth5zJF7TiVaeDwCGckk4ZYm4vj9y0/mwMXYTHNcsxWFNKdQQsRofdPsAfzy1lil+0iLucxESCICGwzhy332u2wEgddw4YzmQbhtfk7gM8H698OxIAW3eGzHn0z0nGpvHbHwEPUkpslGDAGcEMcoRbtPDBaKU4XR5RiwoDabBIWirHBAtBqMxc3Hdjd1L6C/m8haoCjD6u+ZHGoB0zCPzfKMs5zH6H/Gfdb81I4TztoouxFAKfONR1yZKhEJGGBlyKb77x3MBaC9n/gFTQgjXWx8QCUpUt3aqUiClA3Dp79lslKVyBQ6tcjauK2clQvQBYceCqIk3GkUb8PpMSQ/i5t+YcUBebu2KD+uBRai2D26TH/LYSY855ydRzhfDnq/ckwJF7Y9LQriKk7WoKvMW9Cs+cInz+/f5wIc/t6ziJ5OqHnPuZQHXtLM0OTtmnxNOvDHnH/0SW0q8hWcAju+Ch1Jgf/l+731L9wKvRnE1cxPOuXuj4AS7N97c+SYAU0gH4Brj6sYdg3/iue2Fb5ilGrNdfq9PCz5FUY0ZH6d3V04+6WjrB0VVHcME4a1OnGb/7I5nY+m3lxqfZUnGNf2vcT1uWA1bqycAuGj6QPQb5S+bc69B3tlq5UZUeBC0f6K5HdvdlUO2d7uqUPb+fcOM2U3r4J2Lo2twN5KlWsiahU9BABQyiM2zrr6GC5fSBINIidk3Nc0ao77noFMosnvwxc3ljwMX/YotF22M3hbA1pOsJnjfDn2jtpu3gbmbxyWbP5oFvGodM3Th/t0O1njv0fUNOLuhATvOnozvd++KWTnZ5sbkDohoAlyIyCjLYbXbn41cCwAIxMoNZONgCfNe2m/zYorEEM71UpQAE87dbAtzN3AVM7SxVa+RvmQXi1GvV6xzAiM/gUXDyf551TiXO3UCDYehVrgrpwFYE63Za8PxP+TSv3oew4K9L0Q2wjmJXgIvbAqFsnadElQEuHmW/fmsXLjQ85Q0EkMIXfdfoFZ7Zm5+zda/KKJdYzwIdcu513F5gwlVjXaUAiqCkA8vd9+vMcK5m3KBN1ItfNBptGqDCOG8rfL1SwCA0kPFeO71UTgeGuhwcYqEKWTC4sqNZN18Qjhtg5ugGE04Lztmc0/V3dpDLm6rgRRzOwC8czsrfdLIWof7NzgTkTgt561zUtu5j/lSdvvOd6wowtr3D0Y9xjfvHWVdYbcMeF27W8y5i3DeGOtF8aFKqAo1aqUDwOu/W4P//cZFWWOe3Hsbr0XnS+0Ycc7uLxeqsO2h3vHX/W4yCain68XQhgZ0smnRTzWU4WRdDEGVdzWz4fb4hW0vyn9v/je2lrAJLF/+rMGHcB4uH4dr+93quf38nucby2q0wroafA32Cwd0itneTkSlDiOFFMUlNCfFX4mXiBpB9al67nN841q06gjNVaJN0Lbxsn66bQvXW8cNqlL2/uViRqMlAZW0eYA08YcAmBVMheSoM1xTbgom+vF4N3UatCoAikqseScAoKQwdohPVM6/F7iEebjgkt/GbP7OnncAAJ1So48nS3azcdYtQ3lUDi6N3QZAgFJEAJSF2fXv4OOQ5SSEjdOqqNNCBWopc9UeJe3HU9NHY0CnFAwjBVHP07VDMuasd4lrR2zLeZBTFMqEIOIi/JxMzjeWc9KsYQtenoL7i9k1HzllelkQbW7gJZxL6WmuMedyJ/N3lJLY/ZZ55dSo3hO++dImxEsB5p2x6mnTKBAxczhJ2jllqAhwcx0lHqExlls7z9nfAB7llBV2Czfvyu/XkMALwYZwzj0D/DmSOYUTN4+IgN3LhmEl2jn84iqcc99VQyVwOMrcs40g3v5tnMPFbECaW/Y3KNQqtKjhsFH7WreSUhWAzL0oV/wfFEccGnFoxi3HtQ/eunuPmxZMTjLd3i0HaVpGRX5Saxd03SzCrQ03eWT1vCiWUABDzu+OvsNygYKVwGItC63f79FFOHf7nhpjrTuyi+UaKNpb7nsfOTPTeyM3+FI3y3msrOvNGVvuRWPd43yQRIGzGpkAxwu3+X1EUUBV7TsmCv616V/GtiG5Q4xlP5ZzSmX88QN/pX4UH5OnAEy3vWjCiec5XPZRtGR25SlOhZ9fwmrYokCKxJm8LZqBozGVEwTtHy8FUMe0oEMIsnvFWRJ3am2j5Qs5Fh4CfHcupFwW6qFCBoUUVXmvv5sjtaayXrWNGcFAM2dU1t1qh98Ys2mA+Evm2dRphT0fxXitfnkAFBFCML+IZZ1fx9dBJ8TI1k4IxT8+Y+7DdWDvwO8HPkWP7FTcm7MOHyX/BpMl9wRqABCQJIsFnIcXtt0E7yROUShLBCGXChJEMt9Ro3pZlZtuBhOVqnhT8/z4dDuXQFkbFDvVu1vGSTDJVTgPdDTfwSSZfT8kELRYUfu++YbrMWOyz5Y1nhO4yTaW7JCecIYfSVAtbu0fbjnmaONFzJhzN86+QjuxTQAfOZ07sM931P7F5rKb5ZwvaXvODHNZjRjz/Z213wAArK/xeA4T5dZuP04zGktOF0I4b+NQ7sWiwNS4KlRGdW0Sjjaw2EdDOKeUuZnrFCy3JhjTkKK9fO3t9Wztbg9aIFnbbtvWxLgQvg92pWxqZgsIZz654VfjEEiWXWP/ok3UL5o+EJfcrglIr1wFLPs7cHxbI4Tz6DHng8/zV/rJ9RRxCBMpw4Z6b1TMe8NqOdczdmvnufJJdyu62gJCTZo/S2s83NO1M97OzMCmlGR8lepMoORFVrSCxBquMedqBFDZGBJIO2DZxrt1h3wJoNRwabQnqbLjRzhfudts02jh3LZbSGaT47V9PnDd59Wpr+KjaR9FPW5EjeDca1n8eaGsICfdHIPL6svw+5W/R3YP798umpdPcyoZa8I1eH//+75KNwpaF17Wt1O1YcezUV9rfT9E+Jwm2oQ7ZqWNs79hjO0qlUEpU96nEPfcD0S3nFeWm30OWSfUfTqbCrFgSjMkb03RLPO5savESD7L3ja5zrmt5NsFunBOgQgBPj3O8nzI+jPZczwAYsScA8CEftkAgO9fMhIAsF3tC0qBnnVMaO9NvBWNJ6saMLGf+3tKv2/mrj+CO17+2rE9aBPOz+sfPYngjeNYiayHrxwMALjtXGfoQEgJGUpiy1CnXe+FRVtdj02CQVfhnE+uRjTvAyLLhjDa5eGHkDbWZwJdO/aSX5wbNylaxxZcEozKxGo5/2Sb/ypOjcpIb1Q1sT1TvMHi5B5/x5rzQ3PZVTjn4tz7nmdmwedkgQbKxfe70ZjkbbEs5+0EIZy3NY6sB45uAMYwl9HUs0YamxRqDhhVCst2XKWyWC/dJUtVKVBmnXjnjbS6dXUMRC+l9uGzNu0s0QZAN41cSpa5ncejjJsblFIc2HTSGn/MLTss563UrR0Aup2VhYzsZNdJcV2VOej0GWZ9iboKvs9fAJx0Sazmhs+Y8/OvdylNEYNLbmcvYD3poB9oNAGSG3z533lX/cXWdhPvAn7rjJEv/YS7P1OyWRmfb/6f776dDvhf/53d7xix3QaqipVpqXi8U/xCf4Uce7LrJpyz5GZsDAlk7rJsC3CTkQYfoQ8kYFrNOqax++Lcs9yvJZZbe11IQah0MtfP+EMvasI1OBJg91UwRcaYLmMMt3Y987qdsV3HOpLD2QmrYUMYocQ6iX1l2yuYt28eyPne4QfRBPDmHMb+ue6feGTFI1h3Yl3znUTQLEQ8LN2jemVBpdZ8Enu/ZuNjchp7flfP57yz9DmBj0obeoiFiqBmOaeopx3c22r3dJiaSqndm5kgn1bLhJNeuSVG2xEXu2SbbiqEMEHCh5VQ9unma48XBoBr51+LR1c96mv/cKXVpVx3Vw9QighnhczR342aQMWLHbVglub7Lx+EI3JvFNCuoJT6crmXJG+vC/2e+uW77pZ3flyTJIIbxvbEs7eM8TzXlUNZydB7JvfH1498A1MGd3G0OVl3Epkp7L5M4t9ZUcbEzr+831s458IBpHRNKAzIUKqZsr9+xw7P48bEnlyVj7HWynK66TlHdk+z1Dz/5kir8SPz8ss9TxkzIZwbhnBuE+144Xz1c/6Oxc/R9ePxx+WT0EkB8xwHvgS2spAR3ZuXeOVGUBpjOXfZRwjnghbnP5cAL00BOvYDAKR3NRMP8VqqvfUXWHazxJzzDL3WMahIRDVKp7hRXWazeuuWca8HRLes88QhnO/fcBIfP78VmxeZCUf4CYVXcrXWCiGx+5zb05rBOqDXibT/WJU+3aRc3dr97RqLjI5sEhZXJuBownnE3XIeoS71eF0uIpDDTRrry4GBlwPjf+C/b/GQ0a1Rux0KmC/3x9Y8hr989RdrWa5EvWy4+6U2FIGqUpTWlVrKCI3pkw0AiKgKQGWjDA4PX5825MMyz1sR9Bj1DYfKXZvaLee6cHF5XzZx+dsnuwBO8dgYy/nfd90IOYdZprI6p0KhipEQTvVbNtKFsBpGp17sWV2bHLFMfvXvOAzvsS7aM9icSka9DN/J2sYl2BO0HJ/tcE/a2asjmyxXNTjHjoHnMGV96REurlsTXP2M27pyWKEx3Nrfvg3SBlZG9mTEVPQWFejZ09k/RaWglEJVKSSZoGu/DuhxdnbMfgAAwvXATpeEonaoCix70t8xfRB0lEOlOFBxAHP3zvW1f8SWA+QtLTFcEECEC4uq1weFw6sBYrWc980zBUwJKq6Wv8KQT26GRGL/hgTuWdYBH2Mqd+kBiYAQgk4Z3uFl/NjVOdO73V+mMc/Oc/L8hYXlfu97TDhvcHpe8pZzKYPNhQmXvC594kTHPr6psNV75y3T+qVq56k6koJDX+SCUmil1Mzvwh7zn35+lGotjRFc9X7Zc/rISUDX4Wx53Pf8HYufow+9Vju+h+VcCppzTK6Ush6KGCAu78AOPRuZEM7lWPbjtGIDnV+EcN5ceJW0ShSasLtrrZnhdG3VLcbyV9VsOSfABhUz5tw2CHfo6WrFjWY5d6AL325lFs6+vMlu7XqymepT9ZA1IZW/joR5Zm54Dfj8Dwk6mDfEI9spT4dcqyusUbfdYQnwefFuwrltAJty22B/x7IfWvZvgdGh0VwpPdzaXeGuIZihuVIdK/fdjybzkzXAz2JkXXfh44w0x7rasFk7mDZBOO/Cx6ppXgiqSjH095/ixudX4eJ3LsZvv7rTaKJbRSJqBIDkmm+Cty6Fff3OEi7RrCW6cO7lDq/aLOG6Jb1jCpuwvbKqAABA1STI4d5xJ13TCQTLjeWIGsFZGWyyQpsonKekB/F0pwYUBFWLoBOh7HcI2DwZJt8yyFiOJoAHkszXc2NKYEVDV7b4CSloDKGIis+2+3fhFPjHK6lXr45MefmfZQcc2/qPZc/ikPM5y502h6g4oY877veYqlKj3B+LOZed8wP9Nt75Psgnv3IcQ5b1Y+tx7sS4p2WZIDk1gKK95bFd7AFg0R+At78LHFodu60PlCjxqSerzHeRrvzQ8ZPI0nKe2ipcsF01JiwN2rMvU4qjoVNGuzqrjzequc+5GeZY8mZWDeZkpqND8Tr0rHJ3AecP1SkzydPrItaY+hEXKy3rpd18Vu3wQlEV5GoCvn727aXb8eLmF7x3kiTUrlmDuo0u2fg1gZQkJZlu7QSoL9Ms6omyRgCWmG6iJ4TTFNJHVuSg9mQyQAGZRrCv2FSIFZTWWg5Do3iBxbScp3cBxt1hXXfgS/a/0Fa6TAoC07V4+6QYruZmD8zFfYvYf96AEOSOIwVcv9/uQRaHf0nWs87DZ/VqRrd2IZwLvNBdPDoNit6usXzJ6l3uXWfGGNWoTrfRzgHmxqZPAh2CuKp4ZGs3B4Yxl/fBhTef7d0XKcAyYtrju678OzD4Kmu2dp04JoV6nwkhhiz2+X93oPxELZSIivITLlniG8P7PwVWzkrMsaJACIlpOe89NBcDxpmuYIZwbh+E+N8z2d3NEEDMbO1d+mZi6AU9onfc69D6xC2OZFihggLvjR4J4WKhvyRrdxyO0TKBpHYEOjrj6WIR8kiQoxPx9CyJcd+EwxZLi67oqNde9BsOlwMAjtYUmE207zisRlyt5oA1aZIfQTEoE/TJYZPZhhjlAXUhVkfPQv/2bmstZaW+BySkNspyDpjZ2atD1VBUxcju3xjL+YJhTwPQFRqmAoKf5Ia1+/i/22cDADJzUnDdL8Zg+EVm/GK0PA2EEOMZ3bb0aNx9jIYeptBcwvnMz/fgrtfWY8XeJpbHEvgmO40JITuPVzm3dWPPYue+3DtCG292rWFKFDc9UZmkIqyqpuVcZtZeiajga5jybtVuiv2aakXbxjhR3hHbl7FSiZIs4UQB8+T490+/jHaJWqcOsv91p6K384lKVewu242vjztjrbccKffcb9epXZ7b3OizrAE/e1/FsENsjAhq7+4AgHpOSBnewI39qdmWdwX/vL6a1QF/7MRivzvX7nU95/CeWfj2+N4AgPF9c3zVOXejZ7bptRYwvCis+yh18YUnRNQIdG/5/K8O43hFPd7e9TYkew6hMZz7fJQqL7Vr1wIAaChkljfjPAqUstK4+mfu6KIotwjn2oLttqcUCCKCjBRrEr5QIVdyLsr3TmNZztWwM+eO7p1hLyUoB1nlJIBlmY+XrSzpnUUwDnBVBSTJNQs80eYrmbJLLgQp0HS39hrtN20HCeDsCOG8uWiBRDspkvOlzFvBCIGz/h9VLMbYHkGWYZl3a+81uCNGXdIbtz3u4YJDNMu5XZOsa+hcs7X7nxAbX6VkjdF84w9rsCx/N+b900WL2ophoXDO+6PnoGxjOSM7GRk5pvXcsKI5NITccRrcE/QA4GLOzd+B/y6bkhlaFyDisZyXPOuiSdWxuLVb75Noj5Wb40Zr5UDQGZ/PT7xOVBc5tgOsNEs0UlVqFfy1RC5rDnhPTPSJGXNrd38l8JZzP5Zroh1XVSlqQtFfnHYLlJ6g6dYhtlJsVIJEFISjKIFqwk5FnX58XTgvrC6EQhVQhV0TJSoeHh+79FJyujnJunM0S5YTtmn+ecWFvi2gJdmrKqtHz0FW981Yruv68eqq/IcB+cGwnDfTpObIKWYhKq1pWuLP9kR9TRg7V7k/1/Fw36XuinJd0Ek3vKzMbfr4vvxtLhmU7V1CXRJafZkaRkShpnAeYMI9E8DNsSKriym8EQJ0yLYe61Spfp+xTu0/0dvoiyQTs4QnddZm9yRBrqsRGsGNH9yIH3zqDH16f7P37zVnzxzrilANcHS9Z/ukBqYgydBkoyTtZfZhhtWSOZxXZk55BJF0M3QxXmXa7785FH+5fgR6ZjOlple+jl3HK5H3sHfyy3/cNMq8joAeYmdtozY448qjoVDFGP8+31CA/95yH5bt/dzRjqSYbvEkWglWfh89bMxrfBt0dRwddRl7+YRwktVyrkNVgiQawsie2dbDlZebH6KNv7GMHUrEuzKNW0I4vcpNU5Ix8/tKVqUDDq9xNFe1MUJKyXBsM6o8xQtfsrla885qJiVzSyKE82ZDG7kqi4Aq9xixRNM7yZnMY2DKUiA5C6g4qllsbQ+8GjEmgN/O/Tm+1ft5ANZs7brwpbuUO/DK1m7Ev8gAqFUgb4zlHM66rfs3tr2YSUkiDt2EElZxdHe58VkOSqirDFk+A3AO5n5d62LEnDclM7TdrX378iZa+Xi39u3WTNoqvCVworlOZl8yyrNNa2G1S/Z1XkjdX2F1Tf1bMbNABrX0QD3S8lyPK8N0lwQAzBwSVTvdKSPJEM4Vza3d9bjcy94tMZIdQigUSrE6ilLgvO7nacez3tMhbTI0KMfqdRRIP4CGwH7UEXe3XgAoclFqvLjlRdYnyisYIgho8XAUKv63+lC0ywEATPulmem3fw7zErIL5/x3o1vVg4r/TPteJNqtXe/3o6sfTehxdSTDU6tZDt8m+Xz2diz+3y6cOt40T6+sJPcvVf/Ow9q9wj/JrgrUGIqZko4fYn9QZcK5JpDtUb8JANjH5bQZe0VfXHe/NQu21OC0alMANelO7yw5IEHhkkwufytGNml9fKtqWtiEnn09mnt6NEWkQzj/cw/gpUsAvgQY9wComjVX0k6X6hX/zQtcSWkIcW7I9hAgN1K4eVpyQIYkESQHJaiUelrI1x4s8zzePZP7o3eOqXypU05hxKsjsKl0ha1lfA97RI0YLvJXFazBtP3LcdPCCtjD51OHD496nMFbt2DQBneliEV/w094vvOmo60n3HwE5/3UeSwjnxOAMbcZq6lCIENBp0zOwgwgzFnOqaIanUw+25qMN2rSXECznPsrAwg5qWmWc511s81l+7lH3GT5WNgwEsdCQ7SmLv2Ug41za//0Ee6D9gPbxzIRcy7wRB94Q1XAPweellO6CS5Z8gmgoQL4v6GAGgJ1WLBNt/Y0qRyBTiw7Me/WrgtfqbZBhmvAhG27cG4v68BPwuPRmGlfpd3KlNExuc0lgwP0mHNrvw9tdwoxCqc5lQMelvOTfl3rtO9OdwmE3XLe+KHALpx/+Ub0DPIxfzE+W/uXf2erNE+OyC+j1IJPZVUHaIwM262BOhcLAD9JtD/Jydr9EtSeywu6ftP1uDuTkxAhNsfSVU/j7U+XubZPDsim5ZyyhHBu8MK5Liie1/VSY11ehzzrtaRthapS/G91gbGuiy0x0L+/8W98b+j3HNbbPafY5LzBQ8Nflfs3AC4hOnC3LD23iWWn1S3nhBIoVDGuSSUq9hyObfVJSTMnzXocecT2PPKTeX2bHtPebUCUsBMP9ESQkRjeB/Hy4QEzoZabt0FT0YdqGueEvT1TUVIXu1E06iuAzW/hh0sm4LvyIsdmPSu0HgvMP8muytcYwm1Emx6GFNOtfcdxlpekNJJntOs/tjPSOljnBpLqfHbdBHMACCZbx5zq8hiWPf25ryiM3i7qIahxb0bzHlHi8AYzOMUp+rgxXT+f/kv8vKzcdXfVJvTwCkB7CJCdABTseuxK3H3RWQCAbllMIJMJwUdbj2HzkQrX/Vbv91aiyhKw6eQ6BHOWAgCKG5jieFWxLSmfj6R0PBEaMRRKXWrLAQCXbKGOaGE5K8vyOX3SJOtpg0FIadZ8ACXPsTG/dvM2pHZiyt4O37K9M7Nc5gluyo8IZznXS/SBsJhvAEqIPSdHV3a0CKyUAt0zg+ieZSo2RvbKwtH7f2keT4kY9zNJsr4fw0ePYu8ll1jd4PmDKyEmdPtBCgByAiznPPYY8w7W5/v9U3/Eptrr2OlrXBIXSwH3+PFYnNhmLuveAHYFWzvQCgvhvLlogZtDL1vAQzj3dALVFGav+DPQoRdQdhDqHuZKJBEVOLzKXNb30wQJt7rYrIFHtnbDrd0lXjoOd0p9En5sf7ntvECovu25s0gycUy23QSNsZebscx6Qp5Gx9ac0oTyZX83+0F44TwBlnOXF5uuYOCvj8ZKzMK7tWvC4rEO+wAAT278t7N999HsuDXMulwx371udUuzuih6AiNesOyZ2tmyTc+npFvOqUct955htp13bY988UdcFvkZslHuaJ8ckLDjWCVW7S/BvsrNkJLcJ2m8YkwXQH86/PfGuveufc95PSrFp9tPOPYzrkmSkRZMAwW13B9PbXgKALD2+FrXvhjHc5moRrOCSdq9JFEZiqpA1izn1QU/Bg119tzP6C9nkQoG2FhbUl3rWeJR1VQkIZkJZZldvLMWe6GXaWtqjeVoPLbmsYQfc8GmprtvtzfUiDYJb6yX0n+nAvPuBgA8EfyvZdN9lwwwhHM9PpiP3ZXdlK/v3hH1dGGih7uoUd8PnXuzOHR+zkNc2m8a+RPX/e3fR0WxTyUGP7E//15/++i7cmNtNFfxRiWf5PPWcO9rVRfOtUOOqXcXkuy9CakhdEljguDnh5xu3zw3y0yAfvjKwdj66OVGpnQ5RhLagyXeCjqZEPx69U+Q0vVjAGYCUf5wV4/sbqx58ty3ovZRh8Wca3MH3vBi62fO7bdbPmdcaK1E5IZeak2tqUHfKSU4+9U/ItjFroB1+ULypzvX7ZhvLhslxYihgYzUaiFCIRmoNL0G1ZCE753b23KoId2sClo+MS5JsXpYVcyfj0jRMZS/bc29wk4WZgJp0Msry3ZtcpDFhctJTbOc8/Qcb/0cJa7QtZSaW7irL1y8fxxGx7ZfWk0I581GMwjnMQT+CHVq0Xj3dEIo6qvD2FBzHShk9rAeWgF172K2Hbwgz1nOY00mDLd22wOia+pc4p39uLWHQwqeu2cx1sxnmtrjB6wx1c05YW1OklICFje+uuqQkZGeh49xlQK6KaqRwnkfLV/A5IeNVQmLOdfrPLtYGWormNbZUpdee8FFyjxc6fQYr6oT7D4FEJbYugV733e2P7aJ/fcQWE8nz258Fn//+u+u21Yfiy6c88oN1RbnpidWCWjP5WdFb7ge42iQ3TOL0kxt/RsdMvHHTrlI7egUdpOD7Pu95aW1CKn1IHLsibH+WyZxrmpBe2IaGnS4v0dcYuj0+HJe0G7Q3AhTZDbx6NrBXai1W62B6ML5kBPsGSBUQlFNEYK667XnHlYM7xWYCdWeWLgNsxaZbriKSnGq/hRK6koMhcPxDgfx+dmv4I3sfxo1ke9+ZjLuenpyzHMaX2Ejb+36sIKymujx6sdrEptVnY9dbQcGDFeoSrFlSSHCDf7HYz1/RqO9vYrNMlphm4fL6D7ZhrL1rM5MKc630BVLA8ZzAkqtmayv36hOxnKXwF78pNs0RLTJdkSh7sK9Btm5wPH+l9OdpbFCydmu+9vfPVVlPoUH7vvA5Y/720eDHyeiCedKHLlxDIo5bzbu2NRmWc7Vjv2zsT+z9o1LrEspRUVDBc7OjpKQl2OoxKz2hBBkpphjMq9czUwJ4NZzo3uXzbiwn7Fsn/8FtM856eZ8s1uHFAAUSkNn9EjvCT8oqmKUGatINl33A/ZCAEm2ea2bizSA5MGDHW2CnXJAZCCQm+urT9j7qXPdx1z1gUpN6UgpcM4MAIAkc7/r3s+MxfIDaUiWrBcTtt1PaedwAq7HveaatV2v7BKwlZe95hn2/9u2+YFuYQ+kxG85H3GzUbrZQmY34Ofb2B8Q1ZXctTSzFGicW3snzhNZ39+RKLntGe3sCOG8uaAUVAUqClITd5+4PFQBzprjZjmXCBdjDIqdX5VhddX3UFScbmi6VlV9X9vOudVy+8UU3PRs7fYHRM+YacQ783WcY7/03ARWHjcDbFsINSESsQir/31gBZa/bWZb7Tkw27GPp1u7X/TfwOMLiqmAiXZobeLm5gKo/4a61QgAqHbflb38ivsBNcGUHttq3NMRTTiXPNyuAQ9BYPR3o/Y90byw5QW8tuM112288K1buHks1hybcL5bm6DolvOaSJTkfwDKuPJdp/SJteQUvJMD8b8CdGuSbLuX5nxrDr4/jI0lqTWXoaQ65Lofj14L/AWufE73dFbuSY8575ubjtG9s1364fwOX9jyAubuMesOb9XcOAmVkKKwCaDu3l5Twr5DlzxYrsgBs6Gsiz5EwTNL9hnrFZXiorcvwpR3plhzCHTaiA2n1hs1kQNBGcEk73tZJ6e7Jmg14ncCgOkvrsHYx6Jb26RElhiycbwyQVaaVsbhHWVY/vZerJjjniXbjRpdUdkYV2kbtbAqrC4Z3NXh1q7/qotT2HkzOiZb5gs6qZlBpGWZx5O1msSpYGN3WHFazq/I5uqHv/s9YNXTtnwmrH0gGPvhkiSCoRd0j9nOgV7e6Zv/574921sA9bKc25V7+pgV1+sxkIyDwQA+SU+zKCx0t3aJWgXyrGSr27bSbYSjb7xru5uXXSx43UqSLMU0bFw/1sy8zo/zUsphSJpLxupjK431b39dqLkEEGSleiQpsxFRIwhoHbt+31JjfSDWfNljDtP90T9onZTQ7Q/Mqyt1qCZUurl/x/s9jrjZjLk+uRO46EF26BT3DpftzkC41Jp7gS9D2vG225A21szVYFdC0BB7DsteecV5cH1+ELAprsfeDjxaAXSyxq9DCkBVKSpor/gt57yFm7eOSwEguzf7i3UIN8v5ka+Bkj3xu7ZPvMdc1sccu5DVGKVaK0MI580FVVFZmIKiNR1RftBZ07hRuDxU3QeYA3tkyE2O7ZLFGs5Z5qiz9AGf2Z2fDMZ0wyOSu+VcFwiLNrH/f8szt1XHttbwAp37eZ39ipX92BfNbO6R5Oil1PTkOnwsnjE5iiacD/hGlJM6E8LxlB6tdl3vBz7ZkH3iMPfv67VtXOydJgxYEp7wg6mmhHp/DrDg1J8AAIrEBvBowrluAk0ZYU5ucN2//F9IM8NPAnNd3Ln4iaFXnfOgpjSbkHut6/abK1l29t5crXP9F3FmelexqbDccYxkdHKss/ZTE85t48KgnEH48egf4wfDf4D0uktRFzL7cNu5fV2F86pQFQgleG/nPGPdpF4spvCmgWw8iygqMpIDhuAPsAmqm3D+ZeGXliRn33qWJS2SVc4LRXvtjSxgMfNZir8xg8/LYNyHJAJqscJxSqgEeE9d/eORAIBgcuNe1W6/LwDDTRYwv49EYH/+//5J9PwT8RBWVEvd6ZakVkvWySft9Mv25U13+VddfjNdiNIFAFnTOtVrt7ckE1fFgKpSi/A5Kp3FEg8nBQCYgGoXzgf87jV8t9OPcU1HTSBa9KhF+S5pFkM/ybWJRDD+ahfLnF+SMt3XD7kGCLrPvfixdu8pU8FiT/I2tAdzQ/7r9SP99+fkbtzRvSse7NLJkulbNQQJ4Lwe5wEzFgPfnYMkySqUqVQFvvU08IPPjHfGqM5mktPGlD/kBezUJBn3XjoAlw/t6mtfXnEfyNiNDw697mhT3RABoAKUoG+uvzraERoxrPApXFx3TOHcK4u5ZgwigQCyr78eXX/7W3S6UZsT+Y3Njkb/KdbP2nea0cMck5QG63NSv4/Fi2dq5dQiioqc7zE3/W6P/MbSNm3cOABA1nXXAYEAaCTKXE8XaL2ytduRk/DV+wfw+uEnUFHpM4lcl2HA4G+yh1i/d7O4cnn2+zDVWcpZx1U410MAjjkTWUeHG8P0ZLeORMnCci7whKKulA0IesKIJuM2oecmvEoym1RfeJPpAsUL5IS7qTdszXbEiPDDSmDyL4zl2JZzj2ztukBY7ZKt/q1bgFrvDKEAUHwounXQTYhuStZxgw2vNv0YUZAk90mSndQM84USM+b83g3Aze4WW3YAbRD30FJWljTewqXfH0vf3O2pUOGvl6Sxsho0wvXlwGJzWQkhElJw5LD53OiWc17IsqOXMtHjzVoStyRD/IQw7GK54F277ZZzvbXu1j4myz0h3AV17HeUJ//asY3YhUXOOwbE/M4uTH4S0Ygo7sI5AKQEUvCLcb+ALCVbrARZqUFXt/ZkORnXbvsZblhmZmDVE8GlaNllNxwuR2V9GENyhxhtQorqKpx7wd83xKbg8SmbW0jSSuGldF2I9LOfMI/Fjce7yuKrg+xGaiY7TyKsrTzDcocZy4m0nDdnfs6fvLEB5zyxCPXhlp94Lf7fTgCxvbvcOLDRpeZvnMguk13Jw3KuGsK5ZL2PKEX5iVo01EQs782uQSasNoDde8xybrtHAinIDhxD7+Qt5roj67i+aFZiH+FSkkyQntUE4clLIS/JntVMvARce4hHl0w2Bp3duwa7y3wqmgLJhucS3ZRvrKYhNp+RKBAgAaDXOODsy5AsW62fClUwk5ZixJIZxntEHwuB6KE7DZ1HuK7njRZpSTK6ZKbgxdvH47dXD3Ftz2Mf50/UeVRj0SznfomopnC+ujvLyF6UQ1AZw5blVf+bBLRxPRAAkWXk3Ppdo8yZqxBb6V31w/0EfIZ27n1CADmZ/U77PrbWeqcNbHxY8+BF6JeTgrCiglIKKcNZWoxoIWkkKQlElpHUh3l+pGpCuwVdEWYPJ/NCDuLIbmbFr9v2pb999GzwumcsAJRzyQ4PLre2j6KJ43NYOdjlXcLPvV/csfTvwf5MdPAXWtGaEcJ5c0EpTu31p0H0jVuMJfey1WuF8omLrLEeZtuyU0mAJFvkWxlc6S4u+2XsmHMPt3Z9ANO015QCpWHOBabO6vJjp2N36/eXmWtNflFd5pwYJcSt/YOfWePGEgzxKZzzGDHnutDHx90AQG5/ICnKW00fxD0EmqZ8b/wErK7aXTBWOKFd1QRItYZLQsNnRI004Ng+a1bZZIVd24hjLnG6A69k/4mmnS4tYSVPMrr5voZE06A47838XeZELeTyffOTLtWmRNGb69na06jpMfN6kTmh1LO6R7qYk65lWvy55FM4753jYYnS0K3DUpSbRibEIqgGZAKVOkuCBaUgulWbVrO9p/biX5v/hSQpCRKRUFHH+rXlSAXWnzDL5YQiKlYWrUQsSJDF1Y47coWxTndrl3ux3+iEHL8LXOdeZmIfKWDex/w1O+LwGwEhxBEGkwh4N9lECudedZQTwWc7mJJ3zvo4J9XNSH1tJG4345SMplvxAlCNetM/vpjFKOuCTkjzSNJVUApMQdny3snshjf+wGoTN9RyYWxa6Ew52PuXr3NukOxSt5irx61PGfhQEC8kiTSpWohrfJu+3kOZ7VWSzD5u6wrF2z+9CTd+cKOx/vH/Z++roywpzvafqu57x2V3Zt19WYXFF3cLQSJESH7EgRhEvhBPCMlHQjyBCPmSECAJgRDcWdxhYRfW3X3H5Up3/f6oqu6q6uq+fWd2kJx9z5kz97bW7S555Xmf93k9x10zmJV5cU9vGICQpGcEIWcFEJ0nfObjL0v+AgBY374eADQD3mNebJ+rmGznsVANbBXSPmZwVGe4/qM60ZeWvsQojhx+bOSc46cNAdcv0ysSL2x7AafeeRhoxXYsHsJh2JuGAOc9F/62sTdYAiUxEWVZ25woKV0BcqE/kXOJvmieEpZSM/QoLycqfxgExbtvfRzdL72EjQfPwxW3/QCPLNuJzu484FgQgDKXnjGwXA759et50+stVT6kg6IMtnaJ1ow46ePEKwgiOVGPvNcIls15f7rrIJxTAvnsi8p9ykQfqWPNKwC7VurlCwFgRBlIl7ep7DfOB0wYakfwiT5TvY88/RZvr9pPJQO4uiDGwdq7ux0wOHi9+8xwvzKvahBOZWE++IxxUUNOsrWb3isJmxdG46Lud+Ofe36NHXkR2S9hnJvQ7449enTXprDuk8g5AFx3OK9Pbw76fSBewUfbrp6yiIGCyLnsAwddWN5NbYz5isw+YbR1expRn/kNX7cbTKufCut2S1h7278Vhm/RrvaNlVh24Y/BCrqSVFXgBuP0nYdHL17LlUImCIe8XbvBTvo+8JV9B6ktV8zIzOaOzRrMuWAxbLU8SIMo5fhunps9qp6/x/FbQmK8Sfnw2KxQ2goK+dBqkctWqNwNWhkaNlolB8WzXZlJzoWW485NiIpRSjQYuzQcJn7jPvzwXoXISbnEc/9Zg4e+vgmEUTiiv6pj/OktYV3dn73yE3z7mW8nthMAaIYrFHO2Hx/eUhjnmdECmtyHKcMldgSHig7YV4SVlCanwfRFVNTBPkkFEmLWUZ4zuiHmyL5Ldz49YmKgJFvF33/7rh5cf7m9TKEp0mne1dZ/aL6LYtC7jpjIya6kE10iVmTQUPZIbpwra/QBZwcfV7wQOvhebT4dANArCGZj2dprDPbrQshpIWHtvd1RI7iJ6KWhbMzuyWKMBRozXxHHqjNt6tiEp7eaNbq5vLj9Re17HFv7LSt0Bu3VCjRelQe7xLpX6A1LqTF9/GWMqK66DlxwzwUAgKxihL2y45V4aHvM+m7yg0g5aXq0hGTGIVpdcz04QzF3yEGRc86aPYIbfSzZpDh/yvkYXMnhz89ufRaDOhjO2HofqHCiHL7C/rzH/OH3GPYdPt9bCdKAENZernF+ybOJbQ5ql486GFhqIaRVpOrAA7Xvfm8eO37CkWhj27lzseO+++C3hXrluBv/hhFX/QDElf0gxVz/kECaOSkh6oQEnECdfkpyPL8YRs5t1ZhElZx0tzfeWd0IYNKJ/HN1yvZIUe2MQjdw7aHAf0Qe+hk/Ab7wWnnXe5vKfuN8oIQxUFcMsn2l+5iD49BPwvdZQPIiy4pp+eIaW7t++tbuCdiSn2W9FVEWPI3VW5Tk0Dy3WxcCLestOedishGMks8I4rkOX3jY/3RSInGDH5dbJNtle677khDuZ1OBq/d9zey1r+0CAKx/I762qCmRnHN1Qpt2ZvQEUwhBUl3J/hDCJTH5SnnmvrDOZcP7LB5XodjtXlLHIRa79VSIQkAIZ1mMTv0h/1Ny97yOjjRNHzBJqp0L2I1zNZrjGeSPk6s5CuDERt53fAXap6qmMnJesLCgdtSvR82E34YbtMh52F6XEni5+NJicYRwqjg0NFSH1FUExD8AcP1T68LbKgN24YMcMpfxKgJlVGW3ndgwMfj879Wly/XwOSqq6IT1zqMlgdJIfXMlj2hblk9Vae719g0ZGhERz3KYwUuJahw8veVpPLT+oYSjy7mu/jQXx9RV7o8Mqt4H+aP9lNnHh7DJQspynjJq3RuDLipHVtJwLEjHlxyPJqxdpm207erBzo3KvBgDjy44OkLt5uc32gkJFeIyfmJ38JEK56BXZHiiUv+9LvRIGW3l88E5l3Ojr745rjyUbLeJAIpZf2Jg7Wfefia+/hRP+zGdbI6R6lcooYNI0a4zMcxN9nwPT295Ggu3vxSkF5jGudkGG2xdbdclj1yC13a+Zm9IDEQ4bqp2HYrJQ3UURF1lRmN613wnjAZR+KFVQ/H7Cw/Gt989HrdvvhrE7UApBez787+PG8/g6XcudfGNWzx87qmlGNwbs16Ld1173HEY/KEP8U0pYO2BJOVmB8jOqug+VTp3hNHzwz6ZfKxrOooYXKOEW2W3/lurDz0Uje99bxD5Z2nQRysf4P9pCeP8kucCwsQ9mzmv0AOtXwMW/RP4XgPQaqmhLkUzzj0gp7T7e21ATXqjmpp8N5X1AaEeHv1+6usA0B1uecGV1CVShYYeAAzuB3/F20j2G+cDJcxHoVs+Xga0xeTplCOmwn/Wz8B8Blew/gaRcwXWrk7KJpyly2vU2Nw1UYxz1WsuDXUtkrNHsBVHIudi4ogMYoa24jCs7DkaeD6esMsrQQhXP6TEpPo2F7WcWikJHCS+Bc502o/SXYRmYktX7ItSanHS26Xfs+7c86IHCdZRmSOWMVh+A7Z2y5TlswyWffxaFHeGuZws338FuD9iRjZcYxG15ZwH5zz2v/CUevQAgIMvAgB8aAf3wndRrlAdkMvDUZTVANaeJh+bqsa5qI/ecigcStC99nI8+b6XrKfFEcJplyYE3WI++saZ0wMDIo0QRtCW40bdHa+G8+bY+vSOsgXLd4gobnSMSaN8b08Lh/yKpuXiIjKKfOwnR+OCbx0GAGCWa+eV8WVLbeiLUEqw4Y09+OMXn8DWVa375Jqm8+bLT3x5n1y3uI9z41WZKxj7ZR3vt1L6gtDqN/phaMgTsKLywOCzdKxK/5c0KB3RsWUvLfR6QWlLvsM+R1DwvuEJt9/tr27R0XOnjxMHGoaIQlhLlSLru420EVMPobt5/v7oaYMw5ZChKZ5tSuNcbk8wdgYbJFZj6nTm6aLHEuc5KbmiUglDGVtephKXPHIJ/t+CSzVY+4jakJ0+KXIu5eUdL+M9U94TfP/5Kz8P26ge2GY3ttp749eD1Tt1Mth5Yxu179rvJ36AsqrOVOP0WcOxnd6BN9qehFO1BWmiI3It9JiHBuHPqYjRS17Y+nx0ozKOJt3+N2ADj3zLiLk1cm6ymgNh/0+ozw2A1zmXjqeppyceSowxkWmqQ9VsewAscm5GwtrNHUnPtMTzHjYDOOTj0e2LhHN7twVduOUVTsorYe3de7gR/Oyvk+8FRB12QnRCOEmCkTLqb4pqZ9xm/LZS7/IdJPuN8wEThp7dyoSw6O/9v2QMIZyjRs6JEcn8eBgRIUbeR8GvjM8/UTq5GlWVi7Q1B9JcVOTgO+Kz2ubXus7BrXuuwcNtX0bvq3dj2+pWAMDuzZ148Po3gqj82ld32dsmxGrcDizR+lsvUtGoUPKCS3l+pTiZMFdJyKHvEl7GfkBbSylT+R79ns4Q7kmuO0Vhl6/T88Olo0lKT4Z7bYskVC5ZsQjm+9h97bXRmyaQ5rwZYipYZl5vYs75E1ej2LVT36k4Y67N/gYdpB5Pb9iEm2deAkfNXxRjR43cj49xVBDiYVB1JvgMAF73ZGFIU7AYiKJKCHfTmTfhjnPuiByzeHMbtrRyhZUSgh0xJbVs0G+VkX9ra3jex2dZlIwY+fhfX8Y1D64AaAHHr/6Q9fq9+V7NvD7w+8klxwCguj6LbGW8UlFUxlcupp5suTnKvuejZTtXDretaS3r3OAaxnxdDpleOTKQOefSPvQGuJpGGulLKkB/0EkAgJ1LAAC9pAIZA+kChBwQleu70OyRCKy9aVQtqtQU45g+sHDwGfAYwX3+YWHbFeN8zAHCoDWVayVqKwmgdrp5NHnGPGLyaWj6BS3txDDn9ljjXMwjCczNJhlbfVbP7y34firHomacK/wpnrJObxVR1e8e8d0A2g1Ec85tqKv2fHtQhhEAuhWUQjFFX1y2LcwXTjr89kvnR/r2XtW5TnwQo167FulPUZdSGueMhYleTgzS7I+v/xEvbX8Jrb2tkX3Nl16K7J3nA385I7ge0AdYO3WiHD5SikY+dEx+dMMx3HGWGaOnB9YdMjkehm+KbHdZc2h/50Ljfe1dC1x/IvDAFYIQLgMsElw5JdCA/HJiLJ6hk8rqfYbpx5YrSe1Iy17/DpD9xvlAiaJAEAJgwVXxx6a+pi3nPIS1t2zrAqVEN5ZUeLoR6Xl8wxlgYnBWEKOUlsK8qF4vjJwrx8q6gxFYu7h3nV6yY0dhGnKML1p3r/4Qbv/pQvg+wy1XvYjVr+zETd95Hn/44hN448lktIFXjE5ib73aVp6UHU0JIufKJOSWgAFKkblDinTs5cbP9jX7Fn56+LvjoUWP37wCtKYGmZEj4XV28UXVaJcZbX99BM/rXDH8hWDb8lmzse0734HXZmm7l3JBHCAxFSwTqlgy59zcr4zjs+hz8LwiGnyGzJgjQN3QOWOLnE+KY68nxVARkwga5iAjIKw2dnX+W0LjfO6QuZjUOMl+fSEupXhq1e7EY7RmKQqDzFn+9LETMbR6KE4anr5u/YLlO+GiiOm7dJ4CAoKaXCOcYkabEXv2AQu4GpFW2fdVidseJ8V83wxeldXcNGgHyjg3c85nj9p3OecLN7Za7/FWSF9qTVcK5v3Zx/ed3wMAKlkOo70wQupQAuQ64TLe9wav6MZFHRVBusvYIdwir2+uRHVl6cj5jsoJmJS7GZsYX7dPnzlc46AJfrupXCtVTiSMlQEoGsYcMQwcRxkPJKbcmyZmu2Nh7ZKnJX78VLs6Idqhww/Vvhc9hkyKtK28msKiwH+HKtcvBOg3fZ4xjXNJAqeKCX1X14p9OZJHD4o6+v/0dMgX4xcaYKKRHBVlWVkaISoh+j7zA1s+E0dUC+DjD34cH7n/I8G2ulNP5f9POxXIKSRlkiguLaxdvctBH7HvionomzLy02cAjgOnsRG0OtTHWLGov++EeYPI/HHzmCRjvQyiu8oa/gwaK3bqzw3gTojOXUC34IHa+hoP5KjP7RVOUohzfxd/EzkWR1kY5uOOLVeSAi9x/BPvQNlvnA+YDIACoU5gp1/N76JEzgFuPGseesXDano8AaDb4x7catoKHHFpuEOJnLsq+7usaa0qSJVCAYuLnCfIzh4OI1OVnfZdPSjmPIyZEV83EQiV1uET6zH5YJHX8zaIqpQjuzfrTpETPjI9+QT5PtX6rWkj59SNLDabl3E22X0Fl5UyaHjItG8qsltXtQKOg8L2HVh5yCHYe8MNEYXLN7zNHi0g54R9ubCFKwFtt/3bCmFPlbc1gFLKALPmnKv1spXtH27r0MibAMCTSgdxNPSLJIRT7x8XyyBKSgutEDn+NIeMUGrzMcZ5kHOeMhLoUILl2+05hbbceDVy3igi+2fO5jDQPOuMHB8nPZlF+MLmKIEgYQQfWfh91K8eF+SB7gupq3RRSGH0FmJ4H/a1TP/2A8Fn06AdsMi5YVg1p2Qm/8sz63DSzx5PdezervJri+9zKXOZad/do0PKk2T1I8D2NxIPmZd7KYiUO5QA/zsKhz18frBfZUQY1sCNBeoQ+ERRtpU+MDIT3k8l3Ms4BBOG1Gg6hSTDS1KEZZUYjwHbDVj7tsxc/djOjeFnh8Av5IG1j8deG14RaFBSXOKgrAGsPd7p1ihIRKWY89H/Pb1O1PC2yynjTgEA9CpkeNgQks0103BtlgACP6cjasyUp00dUWi6mQuvGvDFQ5U86LqRsW2VEjcX87ZETQJt+WYOfGGcS8eu2jZCSzs4VVh7sM1Yr5cOG4QHDyJYOpbfQ/29ldOm4oDly1A5bZp2TmbkSFTNm4cRV/0g3LjxOf4/0Yi1c5PwRppztbFgVA3i/4s5kIoKwPO1+uRs86vwHw2jyBVJc38wxhiq5s0LN9ckVH1qGBO/z5BhEzgqZEr9axy6Lu4FALjtY8BPJ0N7DrKUWuRCM6PbpEwXJV7rUlTK6bNxnjD57oe175eSMhBGolxkLrgZOOISAEDL9m5t0aeU6OynTZODj2bkHAC2FsRAIz4wXCk/oCy8MqcdiMk5l55fc+IxvJWRcgqK2CLIo6Y2xh4PAAUBfT74jPE4+aIZAICh4yxlJ97G4hkG0PQjSkxqgSdYWWzSek8tsPbGYdzIl/WU95VQhUBIQnJVIZTC28MJ8Xb96tcR45wZ5VJ84sMnHhxBCNf5rMKwasPpvc0i509sekL7boMiatEQsf/WLdtwxd6WiNIVOC8o1eaarCVyHmt/Ot246ROHw6EEmUZeo9ip3IKMqPZgGlr5oo+p37wf/xalrJJKqaniUoITLazAANBV6IpsIwo0UjJPS2jpUzvuTHVPAGiv0UnjPFLEayMe1SLzNvfDzx9eieseX536PgBwyoxhnEgvRVTc5pAYaDGN84Fqg3mfOAePKd+/eynW7Ir2BZt86V+Lym7XvhYzTWD72mTk0Z2/fDX23Ijc9B7g90eVbENABCf+17athOp/d8Rn+aY5679yAWWOOqnhN8FnldTPoSTyToM1dmn8WJTGOSPAHif59zrKfkoJ/K424G/nxJ/gF4AGBX0QNw+lgLWbk2Mpp5XpaD6yKEj4Ysgf1aobnmxOTnfSpCm56CQ4QoqzFA6XQeNLXiv5PiXmdOJHUGBqypZfTC7DCYTGuc98+OJUM3K+q7YC/3e6Az9tKkgxD5LNYvzfb0bNYWE6BpYJdvUkHSlbCwyOQX/Jdp3wLX27NFrnflBsYCCOA+Z7YJ6PQVO5E7ljWSsK3eG7q1RQI+a4yi3j5Xt73lgCWhmmW5BsEiQ/Xd52d3seGwQBMVNTD+S7W34P/x/k4ZMw5/zsXxlXS3gnR38J+J91QEOKOuN9Ns73w9r3S39kQIxzMXCMiVqFAFPHiJyrueNWVZTLQdV36B2bODjnsgNxxsU6wUMQOVcVd9keM8fSmDhszgEpNuhmSTinaILjUDgZivf8z8E489KU9Q23LQKuHsehPG+hZCrC9zN4ZE2k3uvQcWKxY4w/X7nYq882bf6jJXJeIaIgx1wQk3PVR1HL+d177eLI/kKmOsixYj09oaIY8N7p756BG+dBVFUx3tvuuCNy/bc6cm4qMPetu6+sc+TyE+itxpgvSucDcaB6uzPio2ro07iauEMfwIyR9fif06ah0Mpr2xZaDg+iJwXP13Ij93TlkPd8rBIEQmlJ3hxKglrMpkwfHEWKECXX/RcPr+T3Eg7HQdl4FnldGJijR4iKtICik9dY1m295NePrsJPHiivDF+lS+BQEjF6ZzbNxM1n3qxt649h3Ndl5c2CtZts7YUSpJ5ACmP1bSimM7kUP0r77tB46xMxnPL+XvCn4yXMgiPGhBrtbFCgIFRMpl6Qi0vg5xRHqcwlBVDn7MSsqvsB6O/DpTQwIj585RH41C+jNa4DqWxU7s37lw8dBTTv1Z/DFEchj6MOgY8S0S+voBN8xa1/JcqHAkCPmiuO6Ng8a/YIjGgIYcrmuMm89g/rdim+cj1RXS7CNp6mqoPPfJwx/gzrvmKFEpCImVtOPiBMLRxaZyFHE5KxkLuePTd0DJ930Ijgt65rW4e719ytRc69ntKRXM04F7erZEa7SYn1e/sbwMPfCb+vKLG+2pwbMspb1QgcID5POVU/pqdVP18alE1T9O/MB6GU6yWeD0csxL17s+jYxNETrwydigrFOH9p/V7tVm33cs6G/Jo1IJUh4sLKTj/lNP6/eUp0n0VU3h9fhYuZC4pMych1AmDA4luAQUaKYpJRTSlQPRjXXrygdKN2LS99jE2SYO39qWf/NpP9xvmAyQAoHLJTGkavauCRxJxz3qZquhduRm/fxMoXDOOcYvT0wZh4oK4M2yPnoj1PGwuvMSHWOQbJlSK28jImKZiUC751GGoHhQuMI5i9h09sCIzNkvLsb4HeVmBNiklkgIT5rKSidv5XD8anf30c8OiVwFVDgbyILvXFQ2jJOZeyL2sdA6XLq3VWDwdTc6HNyPnap7TvPvXg09A4ZzF51HVnCDbVt8A4V6MqJqw9jTGkGecSMhiQp+jvh0lnBnW0BTajRs7nf4GfGnO/wt75/B6UQM5X3zxzdpBjmfd8PPzeh/HyhTyqbkbSy4G1z46pd33BtAsi21RGfgnBlAbIRdO+lOqegBcldQaBbyh9/j6ap1/ZuAuUEKzuvUfbPqJmRMQB0S/DuI/N9X2G37z6G8y+YTbyXh5Fv4jTxp/W93bESF8i511vg7rlNnnsxmW44VvPYoUFBhxJCy2Dpb5PxrkS5fYYhe8Xg9JpjtKnVb1brr5B5JwAfm8UnbCb+iAEOK7hj/jstC8EaBVAj5w3Dq1OJEPEYZ8KPobGOYFHgOOevBwnPP45NHZtwOhevZa4M/ag8Dyawjj3izrPypaF9uN6RV7tQ9+y7wdwzKhjtO/t+XYN9UQIUKUgB80KDLJSBouBzhfVPHz5WI0xMaw6NJzN9kg5f8r5OGX8KfZ7VA8Kv8QQlsm65ROaa3DbxfMj+52alQDJA/Bx1fNXYUtnmDs+fcLW4PMREwdr69Q3nv4G3tijpGCUqHMOhDD4vJcPcs4nNLXqv6nSyIk25eb3As8oEd1B40reNyLv/xvwLUUnHXVINAXipev5f8lUPmgc16NO+Ab/rqZOOA78PH/+ZOJRygvncvDOlagUBHM/O+gCtPfoOky1gLK7I0egc4Gil9oI5epHAjVDUgdmVEJHv6jc97aP6QfKeuGyAlPrxmh0fl/piqMPLX2MTZII4frKAP82lP3G+UDJkSFDuZffR485gJzoOeYqwQw1c85V41ws4lW0DZUGhxiBb0Cl7Z3cytYeNyCM7U0uzy2bONWy+FrGezGm1Fjz6FoMGx96i81ocypRPJ4Akgf8AOWIep5fUqlzXIpM1gkdH+1i0UwBhYteLBP9LeK594XgKEkahlYn7t9TNxU9CxWlysw536ArcD58+MQPalSzQkwZIAEBYynhtPtSNEK3EoRw1vN9FdbO/7txqXDSm27kWMlvRb8InPoD7Jx8Qaxxznzu4HIpCSIVzTVVyCqw9qyTDRiNVcOLkvQOHZcSay4jb0R0E7EoeDKiU5VJSX5I/Egdcg6X12+Yglw4lWxt74RLCbYV9X7bUNEQYervX9Q63Ti99uIFOLU7nCOKPsMfF/8RAPDwhoexs2cnHlz/IEbUhCWd9sUcsGhTq97aFNf85SOrUh0vS6l94uh4ssl9KUuf2YbO3b047ZdPRvaZBvaiBQn1gkuca5P1vQejZbtiSCt9pggHLrzAOeb6oUFGlOcnbQNPzD2FLcvQ6Q9FQYz79iJ3vFdpMFeiIWJcStIz8CvzlyydJFvjw8O2M98H4jgYltPz6VWUFXUofJbCOFed03Hrc69INXj93/b9AGozeo3vXy38FQ688cDgO2N6+k7e141f2dK4dBZfaZsTEzmvVBwNceSax4w6RptHjhoVpj1olUFiaoDL3/Chw8ZibJO+NldV70X12D+jcsS/saJlKW5ZcQuuePKKYH9Lfod2L7MSyUvb1ZKb6Y3znJcL+kePJCITQmiJMpRmcKIvEGnq6AgM6kSDFzt4hYQgXSBbA3xnDzDj3fp9mQ/iOPC7+JjNYZyV46lJOIw6stW48p6l2r7h3+VIgNG//KW23YoCZF5Z+dVqwG5Np0LWJseI1BK6d8sT+P/jrtB4q7RjYyS183HwBKBmKHeKlCOJkfP9sPb9UkqUnKjtLzfum2sG0TIBCxKRVzerG+u93Wrpi3AA7y1yEhUCP+L9ol9aonfsGONPKuR65DxmkjCMcya6W2/OMrgt49kWTZeOBzWnuU81uheLfFS50CQpzAMEAfWLrHxIpySecVzgM08B7/m/9OfSjOW37NuIuRQV2WCTjYMO076vXU3Q64f5aqaCxogHT4W1xxmG0qmUpuzHPhYt8m0oMLbatdbzxfstwoicm8d6SuRcOYaC5wBKA/Ch5btj33C26XH0FnvhODRw3DmUarB2VXoVD37afHNAOAxjDvctxhhlFMeNPk7b5goHXGdvyvdKC6C+3ocII/ANhamvvcQ0IgkpirlJ/6FVblWEzClNX4iTzctbSh8kZG4+nH/VeaZX1KOmhGJb17Zguwnx7Yv88am12vdFm5NzsQFOuiVFnQ7/8+pmPLaCR7a+9K/XAsPf1mfebIkooGVMo4lzfis38u9t/Rb+/r2wMoU6b3ugcOEFRrSrwKIrFV6XIHIubrdmE09Reb7zQgBAi8d1lBrVOG/diGolUkwtOeexokRtiYjX+wDAGDK+B59SEMfB+OqteM/g0PhzVGJK4qeLnKu6hRsDZc0LxEMCYWp1JtmJ7PlMm7vyRmRaaiFxkXOJgBqzi+GopdJb4qOwbRuKu3gqhEoIZ5LDSSGEaFwZKqnks1ufBS55Dhh/TGzkPCCKt4yd75/HHQI0uxuuMMryikGmzl+MsURH88kHlCYCI4TApS7yXj5AejgGSrKk07R1o/7d5pTo3hvdliTUjRp+6wWC78jP2c8JjHMeOff2ivnZ960qyqki6JDxi9jcos+3blMTDli+DFVzjNRM22/z/bKYyVVHX4dv53/RRD6H+hE8BVSVEtHpsnTaIdPKN6j3w9r3y9tOjJxzWUrMUQ1VSnRot2UAU0QnDlI7GNi7TjkoJnIuUTzqAIwtZaIPOl90N89CkXzzd5+PbFv9ShQGLz2AKoP8ro3x7KMlhTGeZ7N3bfwxA2ToPfHPFWBK5DyVx/H53/P/NAOMmAPMfm/6GzrxsPZ9KVMOHVbyGE/pG/lMLe6/rxb/t/Nv6BI5xV5B77c+8VFZrEFNbyMYY8hOCSMMdaeF8NzOxx8H8NZHzs3o6DGjOVzxNyf+BnHiMQ/o2IY8gPtqudIoI+emMTi+V3jdiQ5rB3jZnSIroq27gGKCoutU7sB1r10nlHxJvOZosHZVvnJruEiXYx65lESi7P96iRshzIIcIYyiuapZ3xa2OtU9s4OeR1Wx1thKYGaZ93Vkb20z8kQJj2SOdc7UNmecTOS39ydyvmVla5/OU3POd/Vwo+DiORdrx7B9APFfvTPKpv/M6vgyeibzumoIXn7LInzsLy9hw54u3L4whNnmLSU032wx5+rDz54Ye2xPh/4bE+f5GOPKjJw78IOccyhOlYzhqAOAvBF5C7lf7BZQUcs5J5F0lkAmGPnnShupMM4ZCE7fwJ0M9csXc56RyiYMz65QjvXQ9eyz6HzqabTfczcACj8JHm0a506MIzhX2jgvRcbmM6bpSiasfaxIr/Jj3ltenPyzP3moEofsuf56rD7hRKw6hj8/1fiNM84j11Xud9fqu4BhMzjUOc44l1xBllepxjbo3jUAANayPtiWUVCUHvOsddiluE66GVWuUZIQjloqrpQltlz71g3lXYPQeP0oDp0RGOeM55yLebbmyCOsh2fFdV4bki5XHIC9TnqZkfOFD5T5LKQuQyw14EvkuZdlnFOnPP16wVXA8wml3PbD2vfLWyJMj5x7hahxThyCQSOU0gsWT9LO4pSIcU4p0WsTxnlwbTnncaI6Bo75ShA5973yorXHk4dR38yhX5KJXv3NQxWIe9niF4E/nwFcZ59MASR76vogB50yVtxaj5ynmtRyIhLVJxhXBsjrEKWgH/RTLz/38jBv8LgPpiCXU9rPlEVmuzMLANDZYhhWBKgu1GFI51j8cuEv8dmHLlH2KflUAla2r99ZGlEjCmZ0YXgNjygcMPgATKuz58f5zAdjwFXNg/F8FVcoZU6jGb06q+s//APhhvXsXqE0Dp6IvJ9He64duzp74cFJoGEE2vJtHB4rIueeTwJY+zUPrtCcAm9sKZEHGCO23PT/+TcnCbT1ecpoxFCUv/+AYY3a9tvOvs1+z6oNOHSjbii7LBONnBtN291ZAk4p5KirDa4KUoRDCQjTlX2b8j9QZGxJohpY1712HQDg36t0uO9AtSup9JlpaMvI3tbW0OD85n90GLSJ6HgzxHSORQOQ8RPoG0/qtZ9jUeK9bfFOYi1y7ojIuXB2F8Pne1hvCJF2RPgxb4wx6STPEr4W7KV6g9R34lASIRMMxIw6SwgwgI7u8P1NbuXVHSp3bAVxnMijosTHxo9/Aps+9SkUN3IjgiWppr6v6yeVMet/TjiK3PhUmFLGsM8AqswZeS+P1S08H3dcoYBGmRVnkuEK6U2BMFKN8zTM7QCwtTPMAz9n8jn8gxUZJ+8hU5WinU+dnhfsfg0AsIyEfUpNSWJgicifRzc+WrLt/Jr8ucsIOYlJU0st6u9e9yTQsh7IJJQgs0mSoRgX3ZU6LvOBjAsmcs5BHSt55/ztS6IbS4iVEG7RP4C2jdHtMZJTCOHGV7xkOSJmjFMHGGdUjijRp8vh3wChyezrpjx5DeDlonOPlP2w9v1SrjAGoCc9JNEqvuLNgho5J5h5DGfUpJToMO+Yztreqn8nlAD1SvmDmJxzWRZLq4sdZyiqg/ikb4MJb/iureXVqc289gTGzWwCEC4kqnGemgTOJsVeYMfrycfsY0OvdnCoLKjGSXlEQX2wpjNVWpRlX4qa0yRTDqYckgI+BcHcLsQj/Nm8Pur/xR5/09KbdK4VpWwayWQi294sUZUWM8Li+xI27gQK2KS8ETVkHm7ZcD/+Uxc6JlwAOPiiCAt2INQBvDz+tH0nHpp1GfCFVwFww4sxYD5dEkArbbJwx0IROeftW7qlM1DGXt3Yig17omXw0shlJ4fe9bh8c8aYdSEnoBFjaFQjd1bUVurOxrqsvXSPW7sSrm+b+/TrjvD0tm03I+IpZXijC4eQIL9XiglpB6JkgaXksLPDHGs5zwM8rzwVKy50OKt0fEQcIP2A2ydJUqTb7Jqezx2W8xXnx9NG5P2tiJybKBITHmzrx93teTx+83K0bAshpY3DquPz6v9yJnDze+ExYz1jDPhdqCDLnPO5gmSxUuliI4rhudOG8nkk5/ncWBHyejdnpnYJn6NWVoU5xYD+W90kWLuqLwAhBBjAjjaJZgtfMCMAXDeaQ6uWfRTjp8NLqMrgFzmE75MLOJP0gR+2HzfvI/y/EukzYel7e5Ohz4wxEBq2L+/l8fpuri9syGSCPHA/xijOpTDOVWSNTDmxtkUZrzt7QlRhMG4JOCeNxZirqeD9ostCsivVN6dqC5a08cj5BJX1X9HvPN9LxZ9SSjoL3HEijfNsiSlxdvPs5APUyPYNZwO/msuNOAA4+XvpGpVAmBtr9Gk556FxThwKZqwt+ZpwrYqroGKVfeCMVPuYljI45IASJzplE8D5RnvPPydhjBGnb/o1de013vfD2vdLKTEX4G0vNoZM230VlaEZwIoXtgMA1i3eHRhH1CHRHOz3/RVomozJlU8nX1/N3YrxKG9axgfamoUK5DxlFDfRG554Igt+XxA5z4S/sa4pJUmUTRIWw/D++1YZ9BSiOw3WXo69XRETLUgSt2LAIPqqYyENB8Dk9jCNIZdtCD4XErzdHRWiTicY1GAPUwzxmvnzRXvegsi5cs+cl8N3nvkOZt/AlQqpQFFCg6jBVAPKd8uKW/DUjpe1bc4hnwDO/Gl8nq0w/qoZwwil7BnAF+QD6MZEN8769vVa5HxsU20Aa5fy3Jo92NZWnlNn4pDQwRDHBbd0W7vVIUUYxbAaPTVCwjIzjm7sjqwdCT+nQ+Ad3wVhBI5p5ABgFpKef33myOBzXRIbdYJ8793T4VASUVzVCJeUciPUQ8aGSp0aASlH0uQND1TkPImxPVJ/3fPjI7VCzFzNN0NMh4DZb20IkGduW4UlT21FZws3EppG1cLJ0PjI0g6OENhRMGCjC64COvlaf493OIoi5/zq98zBvy85EsNqQ8NBNeDOFSWwcr4P/Gouaqju5GDCcM5An4cWLOdr+yNfOg4OJfGOwYMvsm8H4IpraisBY/B270br4zoRVhDhBkDFPHnz7utirx3A2kcfDHzxtfjc25nn8f8rHwgWV9MBlQ7WHp6T9/OaoUPFmPFixk5PCcOGGQ5aabSq8q93/SvxGsG4ffUm/v+lP0WOyYo53RY5z3vheHpmN09dalPTI5SfcM3L1+CuNXcltqcckT6ArIEal873d096N44ZdUxpx2H3nui2e0Rlj6Ez0zWGOHoUV+mXsRDy6Wfx/9POBHFd+HnhELAsetT38aqAs7dnk7kOpjz3bPCZ7YNAQ2cL13MH1XUGyBkAwMxz7SdIxxt1ykZpqvObm6UYMTwBjVYurF0KocDQGZbr7Y+c75dSYhgHbeuq+28YmXXOxRiorM2ExivhbO0zjh6J874soMYzzwP2rEYlKZGbrRrkMcZ5TQPP76qqUwz5lJ41f7CdibSkKMa5SQjXNLq2f2XA4vLVtPvvW0Nv2ITQsC47cj5iLv8fB+VLkj2rgW2vDYiB3rIjjLDKd5SUe+6ScDVeNPfzwWc/RtE6ffzpaGnejK5MGxhYwHwLQIuSS+P8rYic+wqA3PM9/Gf1f8J9og85xEFGeHczhhHy6s5X0ZJr1bY5J3wTcDK46t5l9puqC2dkzDJsZs0l86ozDgURpEzHTx0WwNoBTvz2weufxyk/jzJWJ0lWcdDERc4Lnp0Q8ZgRx+DTcz5tPWdPT6iE/eGUPwAAujZcgq51YR/61As/w/FLL7OyvvsWkP9hEwbjmvdyEp5yiO78QjgGhze63Dg3nvYTm5+InJcUHSslq1/eaeXiUMU2j3QVoo5hk0U+KZc0rXzg0Gg0I9a4Q7Q837fueKOkI+HlDf1EoPVBIsa50UTbM2fK+gwAp3x8BiglJRnsC8zIke4Ka6g/5h2EIlxkqY/KjIODb5wB/P39wf43Kvh77iIhKmXNbj43N7rboAqTZSlVh9Xs9wUfJw+t5bD2OGdCU/x67ghiOgogKyK5+Zg88vY/fjv4TNKstcwrP7dURMzNZy+N80EVg6yn+QxQPcE5L6cRs8kIKFOMOvUONzbYkT1Sii28L58y7hRcc+w11gjxuHqeBjWpwf68zdrs6G2NHOOK+diWEvLY1ruDz5dOPB8AcLSn1C739Ge9o1tHWvRHgsi5MvV0VwB3H8b7yg+P/iEc6pSem/79Cf5fPW6zqJzhptDxABE5V57P8nuVfTHG+fDZwPfagJEHgrgumHS4W9Y8wnysrR+BHidbUmd2GsKABYqG46cPhJg9HbxdldkimMrbEueQrRcILUJ5HfgyRDXOCSGBY9EqfY2cE2rXgeMiAe9A+e/5JW83iSt/0B8xcs6bRvPo1OzjRgdjnTqcfOmEC6dj5BR9wVE9wFZRvU4xHqhDzhwPAJgwV4GdpY2c11lgKGnOQwiblv9lHW0vptxaSals5P9ry2Cu3EeiGefKRJYqV8crhm0vV2Q+o6xhCWDe6eNQXZ/F6APsyklakZ5ZIIRQDZvQEDnu/d/gZTOKO3ZF9gFAdbd9+0WzLsKExomoKTQADJpxrkbJJaz9rSCEU6OmJnRZjZy/tJvnW2csr/v1ttXad0fA6f7+Qlx+GQNqhRPEUFjzRYb/KXwafglFoDJDUTH0QQBA1nU1Y1qe2pkrL6qqRt/j6qHHwdqbVs6JjWipiuj8kdwRc/6cqRhZNVk7blr7eDBi6QOWyLnaxrTM1F89bRq61nwVPZs4dLar0CVqQvP3/KWDedTGrHEOAM9vi5JfJorRpJfvX4+ezvjUIBvKQoXBSvnU7E9p38uF29tkaD1HMd33hbBe87fveCPu8EiZrk0tPX3RPfe5mEacGf2PRM4t/XjvVh55W7eIR6z52lzaCavB2nevBqaeHnwtgqLIKIbVCAW72KNB1mWf98DHlgeG32Z5jeaTGn6l/wah/hG1g9UNx+hBVThrNi+x51Aa71zJxqOcegrcoBxdpKgQhnEL7GN6yzMh4oekcRD5xbLIsAAEsOc4SPaxo4+1bueRcx3WrqIT5CwXRM6dCs391+qka+fPj/85Tp9wOhbvWhzZ5wjDcHzDeNx5zp2R/RHj3CJyPi5Y3uWc5rDedFbocguVBbYyM3DRSGmcZ5Sp5+LPOtg8hODHx/wYACePS51yYyPES22cG4RwnYoTIk1gyVVh7dH3TnwfDmPwU+jLRM3zNwMN/SjtSynTyRb9ItC2JXqgdCCbTonz/lDyHur8NmxCfag/V1l0TErTB4vUtYJQoCLZ8fVOl/3G+QCJzTvevXCR5cgyJKhzrkfOQaLGa0Sap4ImUkPBiJzbFxVZts03B4pFvKKPfG842fWlju6glhUAY4FDTEZl177GjbjWHX3LiQ0YXJOgnJNOEsfs2yis41IMGVuHsTObtIksFSFcsbesEhpWUX7zkDF1+NhPjkZVbf9ydTIVlqoAFnh74zCu0LEYg3HTmBMAALUdet1gCgpvCZ+Ma3sHYcYm5VmpOedZoUi8BYRwqnc/rs65moNclaKNmTTIDjn+xPg9ZtQxmD54Ooq+j2f9WXjJnxx7am2mFjXZcNxnqIOMwufQ17JVaYxzwG6o7EkgEDt8+OHB5ztf24K1uzqR83xkRZsdP/wtGxujaAPfZrArbSwFqdaOZxkwxsfNt5/+NnrcxdhKbwcAVDJOAGgjnJrQMCGyLUnMFjGfoXVHPLT7lfsNZl6SQ08hdJ4dMJjnGR46/FDtsFKw9mXb2jH5G/dhS2v8vWVkbsbIeswb2wgAGD0oni3bNPzGDq5O/Q4GUrR+yfoGa2/bqT8nx6WgDilpnKvG8u3ff1BLNyvyCuexa7krtlcjD8/zQVHEuxzuDKpzdOivhLc2k9Zw466VoIQE48mlBD5jWLWjA+OvuBcLNxqoheGzgUN1Jw8AdAvj3AUJmKn/NOvsYH+h+ajIOQBA0hhhfh8i58KAjYv6xlUqYAxBmUlAGOdK/3SC40S7M5XlVYAwDC9ZSUEVdc2ot6Szbe7YHG20IRkZObfwNQxWUAO/XMMJNjdThi+dwoldh9UPXB6vbGlGeQzSYJf13ymhunGeND90Wt5v2jxk6sYH0IaUJrklrguWi4e1gzFQ5sMnBM21WcwYkYx+nPTQg6iaNy9apq8fvEGUwoice8AvLPDw7YKHyXSC1ZauxKM6Ko/9wNTQqDaJ5eT10zpe1ONs+qNKaP1fIPuN84ESS+S884VX+ny5O3/5Kn7/u0b+RTJdikmKgMAXEeRcd4xXbdZ7gxIqzW4MI6xq9MWVUhOTvF4Cya583/jNZ3H9ZSEctjzCMy6Ol+M1Iw3nQ1LkCKseBn45B4hhUNXaHOeF/F4bMOMc/jnOiNq7Frjvf/pkvDsuge/52kRWyKW4jpfvG1O7KgMAaz/o1HGRbdRilMmmr5l0HhYcf21kf3c1N2oGtywDUSICKgS3MleHU15V0wHefpHzuDrn6u+oSmGE0DRKaIeAq4rxK5U5aSi1Iz7C1VnoDCCPAC+bU6EZ5/bzSjnaVOPcjYucI1zIlwwL+TBahvTG3qM2G+ayf/Gfr+HEnz2Bexdvw9pdHM77rqWXhte3GuL2dkuDMy0hnIyw/8/pvITWzp6d2FV5c7D/W3dwx0BdJurdLxs+bjyHns5CLKcCYwwv3bNO21Y79Qf4+gsfD77LSFuk/nqJdv3jxY0o+gwPL4mHKf7u8TXB5xs/wR0pHzkiOjdIeW1jq/adkvTohYEUDZoJoMVYV821bPECw0ACkDWISqlDQCixO2EVxvVWLyT921Y4AOr6WgRFERQkxpEyn/J+V0+64XsM1EDLDXY3YGLFc8ARnw0i54dRJQd890pR21sg1ETO+eMruNF40/MbcPw1j4Ul8y5+Gjjuf6I/h4VzTkGQy26qU1BqJKYaTGrjvG+R8909et59qZQ4M3L+5OYnceOyG8PzxXwSRM7dShTLSI1hBb1ffWLWJyLHqOPURjAZ5IDLtcISSZfv0+b4MlNxpHzhpClYf/VZA0YUCURn4wemTEJO+Nflb815OaxrU+a0hLXHe/JX0TJ8CWz9mhCnX6VmiRo5j4G1O8yHRygyDi05z2XHjoVTVweYpdQUpEw54mYpCAF80zhPEnOcxUSre7sKuPbiBVizcCc8RfcaNLwmNKptCIZMFVBI6WxQ28r8aD/I9zFQ9zaV/cb5QIlFeepPbvTm5S3wZN0fMfAXPsgjJIQCK17kHkPTWx9I5/bAAzyx8gU0j7Asjmr7YtgppXGs5aFJL5shXW18ourtEpCyvqSWMA9+Z2fEOK9tTJhw7/sKr3HZFlWYwgvLgu0JECGFidMq//4k8OIfgO1RKFopoQ4nBlIVtVQQ/WKu/8b5ACy2NkPcFjlPOwYYoVr+oXpexOhScrICtvYBIr5LkqQ659LQ1IxzY3F+39T3ISLidx83NYa9uG5E+FkoZ4RwYrKCHKMx0eLwFgRejl/fpQ5qK8K5oa+Rc9Xgj4uc+z4LnkvWC8dzVijzfbHR2ivD6KAt59yMkGUF1fVNz/G59Af3LI2cYxM5bsc3h1Fhj4Sl5ryuKfjErE/hm0d8M3JuGhiq1mbjOfS052NTYEyjcfrwOo1tGggZqx3qBNBRoDSsXSr4ca/FdKakQSP851UdUkmQDkHUFxRWOaI+XwqgO68/mzRt7G7XHcjUoSCE2JeTHSH0/9mOi/R9C/8WfPTgYDTZDfTsBXpaI5epA1//GSj8oo9KojubKDyunGcqg+opRzpKnx93FBhjAQkYZ2v3A9Xg9oVbsH5PNz5780LlolFdosoJI8DnH8idDVp6TYxxTdJESP1in41zWdISAD5/0OdR6fB5p8rV0R2MMXz11kV4ds0eLRXmP6v/g1UtqwAAE/IFOGJXEN10KzVMQ1UJpafrxRe177OHzMYYI/1PI6BLWvtlhNgSlGis5vsGV9uiyMlt3Bfs7Glk1Qjgj2fQYM2TvzXC22HTX6acCgD4/f1n456Wb+v7UsPaTXKy8uYY4jrwlVJqk99tODF9GTkXxnmaOcxxorD2F0pDy02pb67ExAOH8Mg5o8D4Y4DKhtLOCHNsjz7EephEsC58aGOwBsnyx5hxLv8/673RE51Mepi++t59L3rerhhenneo7DfOB0iskY3+EJepIgbMttVtyv1KDPSDPhL43wk8HHJ0CW9iXORcOAY69iqL/gu/S7yUVKSYz8IBm1JkDpo0/tp3ceXjqPfFQ3XjIvn6ITJynjA5BcZ5jKEnJ9c+GILUIfA9hlfuX5/uBOkx93Ll59vFXWuAhdiM8wSIsy66cU6Vqco0sNToQxg5fwsI4cqMnGeVxbnK9yMKIgCsFHDSJ1bac/E1JVW8V0p4nXAZOXdreB77V/a0RE4HeA1cv2c0/PxgUKIb06oR1FybRU02Xd/Z2R4qiHGRc5+FRlCRhoZMYw2fW0yDKI1M23VY2N6u0ZH9pmPHFekYnziGR8APHhdCPDfsia+uIafbY0fz3Op3TXyXcQTFxXM+i4aKKO9C2azolqk9UxGdn9t2dWPhgzo3wfLtURLQvC+Mc+Jo/TFtu+KWmglfv0/77tgcuYaY0aP6qkwqpTWuDeOvuBfnXvtMyfNLXt8wzs12llXLV15HRM5LrdWRqipL7wg+FkFxkvMq/7Lywci5gyFKVDG+vlDo75QSDz5zAbdKyTlXxoSbhcdY8O4oJSh6LGInr9ih9CuLrpAhYZBAzuMqSzRj9nmEKnOovzkGaSjZ2ssR4RCT6JBrjr0Gn57zaZw87mRceuCluGzeZdrhHvNw6yvcuU9jeCq+u3tvoGkEkfNMlVa6sqcEQVXHww9Hto2ujc5bUuLqss++YTZWVQgj1GLsnHzAUPz4PbPxldOmRfYxS+T8uO4e9CxZgmXTDwDduC2yP04qZBrWc9fyeuOlRFkaGAG8rrB9sY4Im7NAiehuyh+o7yuHEE69drkOQNcFhC5CHIpMtY/mWaHDlvgeqMg5z7qlI+f8Ok6U3DYfZfQvJcznuhehyjhMKh0XNEAce+kLwOXxNdplaWOvGKJBj36/SAUYNoMjUYfPsly/DFi7qmf3tmolEv8bZb9xPlBiM85TGiZLn96KDUsspSGkGMYVIaUZYFETlhsiYKiqCo+fdsTw6PElYO0v37s++X6qSO8yYxg8sjb5WPN+YuCaEdfE2uby2KRnIgkvkqJYASt+jOdYsqL2AQrFjXM/qBsPAFVJuV1ykizuA1h7X2qk90HMaPpB226N1jWm9t/MCNFyLymhcBp4Xyg4ejTIZpz3CabRT4mLnN+w5AYt53zKIL6oqKPYZYBnKQfz+IqQyOtnBd3zzGpENF0agOIeBHw+MJmw/1971FCbQqpEKTUPYDyy5ygvac2u0ED1fBbkopaSjOKYsaEq5PVkBNJT4bfi1X3t3xyRcuTEplT3NGXGzvmRbWYptRM+zAnbpFE+uCbsj8dd83jstaUBWelWIEuzGFIdRTYUPB89lrrC+6JkmVlLFgDu+vUivHBXTMqSIt0FPuc4xAnIpoDSEf0gcp42L18cn8TWPn0EV6pPn8nXoJkj6xPRGrJrmkRyqry2qTVV+5JEhWYSRH9DKn4QQ6hDOP+R7VwlL7bRsRA02cRidLSBr68MFJ7nwyGGcY4iV86z1YGSruWvH/pJeH44ZmWd87icbH6B6Fo82OFIvu2OHxjcauScxTjQ1ci5v3N19ADfB8D6wNbOn4NEh0jDz6UuLpl7iZYuA+hzOYkxzitZWBzWD6C7lSgLF2MyccNOIinFNFinDgpzob/fKFIJvGjknBCCCw4di8pM1CniW4wjP1OF9rvvAQBUvhBP6GjK9adezz88+A1eb7yE1GZDo9onQLEz3jgP5h2bPuYV4tMC0+acE4MQbvDEdOfJ0x2Vs0k4voxu7jAvgLUnzWGBuJbIuSwNN//z0eMtku8tomNvL7asbOHzj9Q8uvcAL/9f8slyfRg6HWiIdxpJ43zP5s7AOI+s+7YxS2h6fqCcob/M/zzwqQXpzn0Hyn7jfIDEFrlLGzh/7KbluOc3CeRx1EVXWzgBey17S+fYKgY9A8HIGSNx5qVzcPAZ43DM+y0eqBLGeTkSzqlMewbnv2sbTmv8SeK5u5u4t820R5Ph0XJfgkIhy9MkQWqCyHnMdST7+WM/TGiLXahDo0paktIrF9Bib//LRSShBfahmO9oWHET36Zs7qoZAZswMWkf/sL3ceucH4MSivpjuVFOjKgLK4RRV5LlC/FbUuc8JnL+05d/Co95IOCVFOYNnQcgLMMDAC4Yinssyqgiv/HOR+fnQu917rIV/MOIOaIB/L1KZ11SfemgzeD5pU7lVpAsdw6oi+ovHl4ZfC54DK7IJS9lm6hGvPyZr3zrZFxwyBhlOwuiiL5inHd08bnnVZGPPCqBUKxcYQaEc/gk7tiQdYDf2NIWOccm9yzi9csJIXCpq+Vr53afCAA49ieP4YDvPICd7bozqVxW9PFzmnDQqWO1bbbIrUQVlZL2PI/mONTB8WOOx+RGjkIq5TSQw3lra7q8fEoJKElOjZg/iTteLhAl2HyW7FfLUElImqoJfRYtcs706P+1Fy/AhtejjrTlzyVHGB2HglBqj5xnwj6uETaBj59urwE9fh0ctf+ueTRyma6gDBuBX2SgRlSUQkTOM1UBrF2LnA+bCV+BtT+7Zg9e3tCCf7+S4DBQjZ86DmE/vPbvyJJO3F6TAwnGuLJurVqADzR9Ae9t/JJ2KRUt5dvQT3JeLRf9tfQOtPS24KH1D/Eml4DFq+N50WY74oiCwQlKqYl2Z6rKyjnPTogagJ+f93ncfObNlqOjOedqVYtVLuGOgY4d6fN4AWvaUxEIjKb7N0QRGnEyuHJw6YMUUQ1wnwJQUpHM3xo4iGzGnF+EZyG74xcqgxBOjc7KPnLcFalOJ67K2STGlpn+7ns8cu6QVHMYcVwwU18bJgjcZr8/eoJF9m7lDvbOvTmhG5QxdlKOM/V3SidJpsJU2i3XKqfOuelIoA4ngavum/P+7S77jfOBkn5EzksKdbD65TCitukTn4BfKOGvJQQreo4DACzrORloHIMJc5pxxDmTUFFtyS+PhbX3/Tf4vn5+tgKYXPkczvjoyNhzipkacV/eVSfN4xGqhqEJCrtcHBf8AHj2tyUaZTy3UQcDXxe56kFeeonJIw18yxAJa9duPc1SakJKsCCxvkfOJcHdAEXOawclw8ek4azWkmaxv4WCMIaanp3YU7OVR86FAUWNXOKgtigAUiHSJt4CQjjVIDfrSvvMD5SNIIqu7HcYP/+spoOCbUd190R+BlW88xFYHJHKNoEPPxI5B4DbN2/DHZu3htcAr4RAK3ZbI0QjG8Nxlvf81FHTuspwThkk5pem2gocKBi8AR59lmPAU/Kil2/jsL1tgpztkWXp6ure/u7bSx6jRshGTmlEZQ1vm6ztnpYTYe3u8P061NEjbZQ7TiWJ2K5OPZJVbuS82+vGkBP05+7tg/7tEAcZmsF3j/wuAKBQIvdPMpb/+Zl1icep4iaV4kI4TCWB4B+eWJMIa5dcBraoU7nl/pLEhLXL+7Vsj0912LR8r/Z96HidjZk6BL2deezcEEWwqI5Z31DLGCj+suuv+PPOv+kRZyUXXco0skWcw5FZcPRrUVLkkTMnG8LahWPsdo+naPiMaegZwICxA/j4UUrFATViKKL5zZn1OHfo59BFETgUfEKx9XSO/mE+0JTZhIZevSqHWkqtq8PSD+TYSZtz3iwisdsX47LHLsMfFvN8XTeGkE7KipYVyjd7f6QMUVi7W4miOYUk9OeaI4+IbMvQDOYMmWM93jRYVZh7NyH42tBmYOX9wM+mp4Zlu070OA8MXmsrgHTawgjhZKdlmhSMmJ8VxFVOh28Hzu+YyHlsQ8vKOVfmEDkfznh3qtNVpxtxHOCjdwENum7r+h48UQ0hTeScODRKCCfb5WThez7yPfq819WWwxP/WBFwGG14I3QkUhqdXxKlD5WBpJOEmig7a+TcSZ8+EKfLjz2yjNa9c2S/cT5Qso8J4aS80nl+dMAwHyXzrAlFl889TO2eBcZuSoxxnqm0DNbTrw4/f/xB4JQrgff9NWyeknNOKMEpn5iBo983BU0Cad88vDRETdpw8hmmepZL7wQeihIyaWJ6JSvqwvylUoRw/RDTOD/vywfh5IssJS1s0lfjfN5H+f8BInj5wLcPw4U/iJ8ogzJnSvMpCqBeDmM2PYLm3YuDEmqMkAghnERtUObgtQnh+/cLedQccwwmP/oIaKVYiN8CQjjVcJXQYSme7wVRAqlkUEWbcETmnwox/P2OXfjNglXadVTjPDB66kfx/xXc8KSEgjHGI6SObkxMKRQwqRD2eY/5EUVclXmKMZ0v+qnXUfU8VzEQ1DupsPYtDSsDxnbi6bwUrQZTtuMPxnCcGrmnTBeIE7eB4QsHh1BAFQWUFXVpzZJZacQlLh7dGEYxSUY30kwoabnG+ZH/OBLn33W+tq0vOc+mSMVeRt9KwdpvfH5D7D6b0ybXXcDhPU6iI0Ea4jINYv2e7kTIuDTibfmaC5ZHa7n3VfwYWPsbT8RHkL2C3qa6wbpRQCgJDPOeDqPayK7lwUdmOh81vg2CF/1o3rCUAGYNB37Rg5+tNvZ78CsGAYRGcs7/VDyD/w6flXTCZ1xjf0bcR4lS+mK0y97vE4L2qRySyw66SP4gTVRY+6LXLSlwcl43Ia5xcrC4z4RjsXBnSGJXKnL+8vaXlW/2/uhAKaW24Vn+wa1A0dDFHEv3H33ddfy8Mp1sJtTbzEF/uEa8h95WO2R5zxrg/iu09dHGxl4E0HYnZ4FnKVQt2a60Ou7XDv1aZBsjROv7zj2Xaft3dovxbVvb/UL8vJGmHCmASCm1wBGUrs5756MKkoVSYOJxIEfr0POMX4RP07G18+s4URRgYJxn8Mhfl+H6y/Xg0FO3rMIbT2wJUmM3K05DSkXkPO1CnjJyvuTp0OEvLx2FtVuuRWj6nHNLugYAYNIJ/P8novwN72TZb5wPkOxLQjhV8Xm+8yNWwzmbL7FYOVm4SCotZkiMcW6dfI+4BBg2G/jMk8DYI4CjvggmGRqBMOdcGOdTDx2OuSeNCQarQ0oPzoCtXbl9ZW0Go6Y1Rg8uAQ/W5Imr9e/q4jfgxnl43eYxdUHeTknpK6HbAP4eAKiozqBhSDyigVbyfTr7rAdGHFDfA2FeEElnhIZwS8ZE5FwQnkVg7QXQykpkRo0K+tRbUUpNVXJMQyfv5wOFMCCHU9ZHl/FohZn/123kLMtnAABF+RvP+ilw/p+COp+EEDAw/GjJe1E39QeozdTjQ236/PDuDh6V8Ao9oJTA6x2BQscBwf6bP8nLYOUMYzUte3uckqZuvnfxtqAko088vDD2bn5MIQFBAmBUx1UYwz6Qqh1SDjt7Ai78xtForAyvfcQ5k4LP0tjoCwldS64F27tCZl6W1/PPVecHY7Qs49xm9M4+bhT8Yv+NczXvFiifRV4VVdGU9ZGfvm01juh2QbbGQ2w9sU6qDpwkpVUa5wWLc+IL/3g1+NxfNveVCpFegx8q0klpXabPNMmB0tNhPGuF9M03YO0akRqAXxQtrMfGsYxRUUpNH7+UePAHTQZGHRLJOXddboT4Sim12FrM5k+TzkhXNc5F/4Ic4wS+VNBls4zH2amQoe1pt5Rt2sRrtuPpn9vbZcpYEZmWDkwhtpJkqsjUD97GOOM8xDF4QTlLNwJrtxnntEbkh/se1p5zLjZ/8bLE9gRNIQQnjz05+J4xDEei9vutryIi91zGCXw3vxRsss1H6qrjp1Bbg7K+KXXck8aeBABoVGqs+wT40ilhvj0Rz/RTsz8FADjt36eJm9kj59bxRt30aYDEgFiXi9JQLyXv6ejnTmrbAq8cQjjXQggnqm3AyWDVSxxVps53BYEgknNVvVJRRCOESyMpf/uiR0IEjEQQRMh/bTYFLSPnPE4O+QTwldXAmMNKH/sOkv3G+UCJzTjf8FSfLrVjXbv2PdejD2pGHBy4+DfJF6kejMkT+HUObnqo9E1TTGiap/KSp4ERc8M2KfuCnHPGdAUmKP0UP0mN2PaMaI4Y6Mrk/4mfHoNzL59Xsp0RSVLc1NJUpdjaVSkzCVKWUgu/Jyxq3Xokrs+R8wE2zpOkac8SFLdzA0ZbRF0KRl0Q5oEwH0woTQwkeE+EcbgcVWDt9coYYLk8IPK9iPPW/UY151yWq5JS8AogQpULI+dAjSjl44ChKPbVeT4e22gvA6hGfILFvaIOmBOWYSMgev67X4Rj9Jkf7t6L8zo64RGRZkA8gIWL59jBPALTW9D7vlnvuVwhijZ+6yubgznEJz6YVy+O4SJJ2tQoPMBZwE0I8/kH6cq3KePnNKOmoUIbOjWNoSEhc84XGnW3+yJm5FPTV8s0ztX3KFN6qEuRM6CM//nZQtikvjIelSShvRknXeQ8SbYrefXjm7nhUejlbXx8WUylAYSwdrWdSQ4gObxLQUL72k89z8eyZ7fhJ399Ldj24c6KIEVk3Kz4/MZ8t1E+MUH5/seVL+gblPcczTkPvzfXVuCb75ode10ZDWeg8Ip+xPHtoAivyIAhU8HO/SMABCVW33PIOAA871+ytZ8yY1jMfWLEFjkPCOFokHfeu41zO5hLmeeEqJnuLq4zPL92T2h85OPTCqwiIc1GeTEb6/kRI0KI+V+X/DX4TGJKjUk0uMNYaMD6XgTW7ljUB4kia7vrbuRWrEDHg+nzun9xwi+Cz+bv0HrOdguRm0zBU56H1ThXfoMv3lFVQr3wUXV8/s2aBK9P/wJY9M/I8SNqR2DRRxehUalmwQDMHtUYfJfP9/rXr9dPtuXTF3PwbXNC2nxzQGC+lZelRKjLFZnCp5HEAWjubYcPSQiXMnJuGud+CGsPNqmQ+oAQmf+bMJevG2NnNoFQgm4/2flt3r9cKZsQrr8oR0KA2phSs+9g2W+cD5DYInekZU2frlU06l8/cet6/V6EorI3gd1dyFEXHoI51ffg0OYH+tQOU2yMwVK8omqcCwVc8cgDCAY+TYicu5JVPWqbpxdT2UuaDFQGTDkxvX4bsK6EY8UwxkoJdYhGMpRYYsxcPDvSlzbR5C00zqeu+ifyGzaI2yt9wxUOGsYj5z51sW7cmShkauGJkANlfMFxhMNoyhZgolJC1G9vR8f9ok+LY96KyLmq5Fgj50SPnDsAHj3ml3h6w2aecw4GxnwM84poFu0/fpq+6FA1ch6zuEtYuxQGP1CQVXEYgwfOzk6IByhGgFxYzch5v8Xo5lKpYMSHL+DgclGS9dbVvHeA9wWzXZefMhVJInPL1ciO+jmTFrWSQvJ7jo/fyWhpQrhHfwAImKxqnB/7UU7cxhjDgr/pNV23rmq1XuoDhyUw7Ir5TUbf1revT25XgmzcG6ZxZANHY+nJWr7/qqyDpposPnjYmETjfIco0ffvV+zOKyl9QUAAPAK04G/LcGKvrtRLlEqiOh3TtwFgZnNMWbDgYIWl3HDuqJH0aYOaMGt0vHItHUM+KHzPDwxvKZQUg3XbJISrrOD9wGPhOp0plwC2dWPkdzTeyPO8fUKR6eBG+fYbudOdDdVzq6kfrqMNjQz3vb4dH/jj8/jHiyIyl9Fh+iVFRpaNNdQWOb/u5Ovws+N+Fr1GTPBApiURhEAArLw/Amt3bZHzKj6ndT72WGzT5zTb885VMSPnWtR+0Hj9YC2FL/xNNli7Zxm7STGNnx/3c/zsuJ/xqhWqkfzI94D/fMZ6jgnR9ynQWBWOOxo32iypiq3dNUGtbU3KMc4JBYqK4V8mrF27bb1wNLvRfuYTgmxKWDtxqCVyXoi0y1NshLBYkX79+edPwtIV9WBw0NJjEPed9wdgjHBO1QxVfkgZzw/6fVNFzssppTbuqLLb8k6W/cb5QInVAOyboltVq08ObXty6FbqCEtYcCmprMngmPr/g8MSogpZC5TMkPnv4UpiEmxPZc6897rFyPcWg1qLgci6zF487FHmoBGxsPQJrWg+m6TIlToRy8XjmV8CN5h1jA3pTcfyHNyG8Hq3FTUuhk+sD8jOrGK2P9duP66UvIXGeaZgWTgBLBt9AQAEkfPeqmasm3AW9jTPRq8rPLBMlH0SBtTBq+OfVQAnewtyzpMi53kvH5AaSiFgqMnWosH3Rc45wyNtK7A6mwVmvQc493dRWCmxRM4NeXjDw9jcGRovvV4v/lYfzd90wKGElEJEzsNry9rkZuR84pAa6z2TxPd8XHvxArx8n04k1lCVUdjaffjCkBhVpFj54vagtvv/nq9HCikBFhnlsipKGNfVDSKSoc0/4cds0vgrV3w9wsQYMCgjI/tO6cj5Uz8F/sLzf9X3+PLul1BR7ZY1fJ/vuUpvi8cjiQQkUI6lgn/da9fpcF5DTpvJo6hD6vQczpfX78WHrueR4IxDcNIB9mirTWTOuUMJMg6F76fjcvzpQysT9z+2Ij5anyTdZi64kN1/Xo2927qCNAybmJFydX10aIl3Pv7o8DomrF0Zl107x8SmNW3IzcOSHgH9FZFzGiml5gXtMnPOCz4vm5Yv+gFCwYlB0MWmDXSFz903DDyfENCsrsuYfXnE9hBRMHWaFzh9NuwVEXOJbDv39/b7mxLA6PV5rKcY1TkyNIOh1UMj22NzztXIufJbzcj5vNXh+VUHHYQRV/8vaG3pkrI3nHEDXr7w5cRj4uqeA4iWvpKlXwFNkfIsa2VR+c3HvCEdU+G2ETUj8O93/zv43ljZiFPHCx4QS0lQ/Gg0UIhWeZARZoDnto9U5haz/wSy5D+RTTcvuwx3/3pR9Nhyot4vcCQJvtcA9LQqxnm6sn1DLrss+OwOFf3IiZ47pW1Lalg7HDcx51yKVkZOWOe+z5DvLWLTUv4+1LWvpcdw8DVPDa+nIiT64JiIjZzbEJ/llFIbcWDZbXkny37jfKDERgjXR+PclJ3rO7DwwdBDTf2CVhM6VqTXefCE+GO+sgK4fEn8foQR8yTjXN23Z3Mnrr/sySDnPJBVHF5Pn44vpyaN87U/49AmmWNTlpjKcKJxrig+5cDHyyR5Ig43zh2XYvCIGIOHMeDBb2r5Yf2St9A4j1NwOuo4lJL6PrJ1htIpjDXqC0I4oSjW9SREcxyZc/7mG+dqBCLvG7B2v2DPcxTERpQBj3QqxuupPwQO/FD0eFo6cp6zEKcENYaVtA3KOPGPrHOuRmFk5MyMUI9vSm+cnzh9KH5w7qwARfPy/Rs05ExbT5gn6BMvKHM2qejg4T8vDY5Tmd/VtqlSqv56SCSpXEetHGExzi85flJkm00OG56c67Z8ezs+OOoX6Fj5bTCWbJx3bXtN+672mRE1I0AonzeGjgudqJ0t8aXN1nXq0FavZ1zkuhrjc4wTDQj7wsyRusPovb9/Lvj8g3NmBXnh8lnPNo5XRfZhR5Rd8xjDzo7w96h15/9z6Xx89+x0pJntPX2DtRfjaiUD+Mf3X8DSZ7bG7jeXfA0hlC/B91IvWJ2dLHxiGLCKmpbZ5sSuS/e0fBsFFqJMvNadqMjt1o7hkXOGVx/aiEdu4CR0Mufc83x89bZF4jg9cm4OuTR2RdQ4pyCuoez3cu6LbJ3Ik1URR5ueQybfwu8vryXHTmUIh04U2beNMbe3d6/l4DDFQxf+fBorGvVLK/8DWHvTZBSMh3XIqvBhZUYMR+O554ZO5ARxqYuKEmRmD29IIMF6zmC3vuNS5YuSo2zhK1JHwXTBgaiubzWZGq3Guia24EG+A3j+ushm1fBkBFp5USmymkSfJKVhDUCPmrdvDeHzFgPbJk5j2CdpNde1bZFzgI+rVJFzSoGisV4EOefh3LhUIWST3W/b6jb860cvYekzHGmpBswiqaROhv9m9QJye5ki7xOZpmx9njpcH00TdVPH8Phjym7XO032G+cDJDZCOOqWNuBsUPFSzLx1SnRlzIzB8QfWNAMfvAV4/9/ij8nWRD2uhjx/x1oAwJaVLbHH2GpO8pxzZeD38PNpRzxEUSIC8tl4Ba+kmJDzpNxKzTgvI9/GUnM2SSgl8H0WdVio4uX5Avuvj5R17VhJWxpuAIT6HoZeEWVolbK7aRZIUS+dIo01yjgETkZxXC/6vBrfJ3Kug8j5vnVAbG3tQaFESE+NnJsG8qMbHw0UwsvnXY5TxpyIky59I/BMr6ww4GMBH4NxE2XFM6PaUs6YcIa9gZ99USsr44DxaBYhIPCgZizKnNOckVLz/kOS5wZV/nzRofjIEeOCKJtX8NHxnM6oLQ0YBh8ggJ/CgWkbLo3V/PkVwdDrRA1M+RzVsaYRE1ouWhmjWJmSGL0C8NjyXaDIAl4NwCi68vbo7DNbnsERD30EL1eG70hViBfvWgxCCXzGtHm+qy0hpSaydIg8YGWeU50yJAGK/riIRicplT2WPllMULwk9NshJJgTv3tn6Bw+58CwFJFDSYDosInkKACA5try4ZgAsOSpeOMbANYsjI/IRyLnvaHRw3I88ts8JoyYatFnqXh+6jGw2Rfo1zEi6WkJropd7cj4+ljgkXMfz94ekqZK6HvR83D7QlGKTUaFxfOuNxxkSX1A0qSZxFM+SMRg9/L8+5A53KBzlHnT37IYpy79Bm93kNNdXjRTNc5nNs0MNtuNcGBM3RgAwKCKQWiSjiFhyPzyhF/ilyf8MjjWEYOLQsFEHnEpTC1v1gb1WYkf4pShW/RHNr8C3PlZ/kJb1ofblfXRW3Zn5LSixZnOjOopsRKXp/yUJWVAMTx9Ej5TIDROVDTD7s0dYAdeqF8jKdWhr+S5APDwt0VDM0aM2gABAABJREFUUhqoFqcZiXnPFa6TWDIyECeerZ0p5QCzVeFnucYtenQT2naGDgeV1yjjehydF+zMAHtF2m3rBuX+6X77iMmhY+KZW1dp7UgU+X5SGecFoLoZuOx14KJ7UrXrnSz7jfOBEptxkCK3Yu1ruyPbXntko+XIqHziZ8fgrEtK5ClNOx2oTjDgU8icE7mCXtsYTxBiM855zrmyIcujcERheB08Uo/MyXJavQqrZ9ly9RijIQnvQV30zQXowW+q7Hb6vmdLEPKZt6EEzON1nmkcpDYhitUnCSLnfckN6J9QVoyQo6jS1jApiOBIkTA66gtCOKGU5rLR51V74gkAwsXQWi2hj9KT9zD/6gW44t+vxx6zZM8SfPKhTwbfn9j0hLZfNdyH1QzDz0/8Faqrm+KVTMk6b74qRQH47QJ7VYIxdWMiRtbXD/s6MGSadj8HPELCCeGKIFD2SVi7UWOVEIJfXDAX13/0EHu7LaL+hu43WjGhEP4GlRAO4LnnUupiaIKTFEMCoEijzjd5jhZ1L6E7pFKeEDXO302fwTQSztlVWUURYw56CjHG+Vaeh7tEgXreuPTG4PP3nvseKOFGoGoIupbxIIWYS7yoi6RGznXERAoi0ITnYnu+SaXRPCVy7gjHg4oIuWj++OAzPya+fWMGhVFjs3zdmyFmt2SFsB/2+Fx5Ved6zekujU4nE1Edcn64JnZOq4XnUTzX8WEU/NCJ01rU6ykDgMeyoEQfC3T4tIizP0D0KZGp59ZyKKx0hrQZSIRE41z0rQgrNCEwV94tD3KUhJeTEPvwum3eSFTnuTNEzkdlM2grxvmSPaHTx8zVllKfrUeWZnHulHOV8c//u9TV6njLahuUKT6w6iY8VKMbiy9OU8eEQCKkZRCPkfvOvw/3nndv6QNvOg949SagY7u+XQlYeG2bUeX7uHlv6BjJwYc7hPOdPHwgb3ODG/bDJCeelbANALJRKL8tcn7d9p2o83xMEONHEleO3zsLt1z1ElZ2zdcvkomvEJOaqT1JUjuCos/Ea7enCVVXOGAseW4EhD5jogCFHeErc7WKvlz7qt2B2DQyfP4OZcBwxVaIM8JT5pyrVXryvaIaTSrjvAzS5Zf/DHTvBhrHpmrTO132G+cDJJKQqmKaUpM0RcFIz4g83P3r17DyxXRQ7sqaDJzMwL/S0dO4oZzEMG4r9ZPrKuretKO/BAAgB/+/YNP4Oc3mlQAAjW3cGzdudjxbLoB0hueWBHIe1dNqKqrP/TZUDsz7eOXBKAnlhHC57iKoWTNWiq0USn8k8FK+FbD2eIgXwBXEKDFgmHNOCIErDO/Xx0evQwSxXLAY70NCOKmk3fd6PBHft57+lvadpUkzAYCmGOg0jam7rVgAO9rtcGaHOJH710rFSFFKJSEcIQCIB8JKR84B4LyDRseyONuk0Ku/1+Fe1ECRRrmvGOdjiva5zFzzqxRDjALwDOP8fd9RYOfKJUspD2t2cSTHs2t2a1BrKdJwNNMVfp29Fg9WXBF8P2H60HC6YA68GEI4aYj/paEeXeI937ryVu0YJ0NRzPsamWQSsooq5GLMd+DUroi0WTVUkvrt1GG8DyX5veaOaYxsO+/e38PP2WHd0tCnlCM4PAa4Yl1515wRGKekUbiUBvtsUlAU3bQl/7TzEyDtSTL9yOEAgGETdKj19m3hs1+T4wZF8+hQQb75u8/j2osX8Ai6dBhTN/I+u/3G4DMlwBuv5LGw671Y2HV+eK3d10ba1ek3w2f83X6n8P9w2bQFoJOO1foOEBrEbZlw7ZVR/YCIzZAkpmlpnHsW9dI3dCA/If1gYdf5gYEfcEpIbpe0cNuYnPMIq7giDnXg+z48n6EyQ/G1Mzh8m4JiV09o+MgRRMBCArVJJ+C2ep5yUuVW4RMPeDhIyTkP5m83pcEXI2PqxmBsfQojRd7690drZH0BerDQAw+8lCfrDZGQvYSgYiJPgQlemUqmKX7vb0/8LW44/Qb9ng9cAauMmx/ZxBSSOkb4mnRMTy+e3bgZ1VPPABhDZhtPtRjczVOy9rYY795NMM5TkFJaRUW+pexrNofLnj//xXpsdYa//5KM7Q6Npug98WMARtpMietIvp4zzuRr2obW8UCnYlfEObtSOibMilJAQuR8usLfJJ/ZW6STvp1lv3E+UCI6W/Oll2Lkx49NfZrpSdu41J4bZb/lm9PBqRjotui4FC/GONIGbIXIm1Qm/YNPH4cPfe/wyHmStb1xSHy0HkC63O/Xbo7fVyrfZqUoeWJOJmUa51TknANAy9Yyy8P0VQIazzdvIhyycyFGb36c376Cv7thE6IpCs27FmFN/anaNmmwSVi7jDrlhAPqLycr01fAELrvoft7O3mUIclwWd1qj2KXlKpG4DuW9BCxKP7p6XXRfULycWPMElle37aef9gdEmk54CVzip4PQj1QBSbnxkTO+yLWEjdCQkI4fh+moCfO6rYr0GbOeXVWogwYCAg8g3yrcWgYySJlRM7vXbwNjDF86PoX8H4lr1oaLo3VfH7oLYaGe1WnXWEOWOlB8ejy5GoLe1wHPx/caN2XrXJR6C1qxGRJRiVRjHNCvSDXUIW1q5F/3zI3PLhkOzp6C0G0NAlRcKBinMvHW8jUYedPLZBWhMqpK3LOfcZw9hweBf7okeO1Yx1KcOJ0DnG1OYceWRoqm33xzT30f8lcK3GSrXThZmiigtzk8nE8//zQGdexh/eb9Yt3axFh8zoPt14efHZAIAPyEbi7RdbmOAPz37zT4IPCcTiLu2s48Z9nM9FDQ8eBHGOrd+qpRlK8hDHtq+UwDWnrta+T1C0RQZTj9v7/4f+3x6OYVOle9AaW/XMkiq16XrWTEHmnhMJjHnyf4cOHjwucUpRQnDj2xPA4SWYIJedccXRdNu8ynPYqQ6MCgJOlPvsbOS9bunfrOdVCX+nJtePu2ho4YNimOAzaHAcsz4+XddpVh5d8H8eNOQ7zhs3T77Vzub0NdcOj25T1xScAVeefmiHAqzeiY8H3+T3FXEaNCgRwE6K75ZT3mXiC0i4F3ZSWe8hyXPNnPh3Z9vSI2QFHSiknIqEO4PtWAkb1USXp4kD4GLIF7lx6ZfOhuq4cB91PGTlv2R5FeVqd319bD7zvr0rD3rpUy7e77DfOB0ikt4u4DqoOPw4AkGOl8zXVAVfKGxaRN4kES0bM07K1q6IZ59L4VSYJx6UYNFyBTwW1rkVkLSYCE97YAhmd91GzccnXCBpjIWO57eP8v6nEllkjWH0OcQzBqE0fnUx5U/7/TTTOZy/9P0xdzaN/kpl1rIUXYdymKLFNkHPuy5xzwe4vFum1w8Nn2CFL0gSEcPvuN0qSpF5LFHmfiJUoJd5j/VLN8bii8Mmg9nIaOXT4ofyDAh+jDPAIQX218KrPDGuFB4Rw4jd/WZQqM8nAUonRzA8eNhbnHjgSowdVRWDtPivtqXeMRV8qinK+HNwzPPZ49VGnyYmTJGjr94TKxw/v5WXMZPTouW2h4T5oi+5gArgCJqdyp2InMvXR+sNr29Zq3zspBdq2RI6jDoHvMc2Jm++Jn89ktMmUzkJodKkOC7Os0tpdnfjMja/gq7cuDn5DLFN3jKya8j54e+1OZmnwU8F94PsMzYKxeUKznuLkUILm2gq4lGDK0ChEViUvLAUXDeTOzwJ3XwZAGMl9EOYzEIck3rOS8uddUZ3BMRfoRFode3vDd03dyHV6WRiRdxAavTQCEk+WuxZtDfrPIAUGu75qJi7DV7X5bVgDd6SaY01K0txjRs6dQw9Hxdy5AIBfbOFrQMVoHQFHnPB64zaEpV6HdhtOT5k3nbM7DUzZ8PFLAABrfqPnVSelb1BC4TMfHmMi1cIPtqvnhZFzJedcmbepzfsnDGCnoSG6byAk1xZ+PvRT4WdhnP/2uavQ5jhodRyMKujzyPbtfN1zRTdTndOJsPY4J7aVEC7BOGcecNfn0StTAYzqAoHkk9L/yjDOj/xs+FnV51Ia+MSSniiJ4VQ5ZOfyoBpCyci5RBtanGGqfXDf715HV2sOXW12/ViudbRFcfarAaV+wtqT7qlJ1SD9XgGac79xbsp+43ygRA4mSsGaOLR9zwulI6R+P6B5b1bkXJb9KsYQUgF2WDtg2CFyIVOM5Tio6chtz2DYjpdw0BElFrWiZXJaqBDgPXcdsOK++PNrlVIqNo+snERMA7cEiZ4p6nOIzTnf197Et5StHSCZeHgYtSAemAJrp4TCFc+JCpxdQbHjGt/zXn6PfRQ5v+gvL2LWdzlKYu3ufYNsKMXqrUmCcd561h/xT+9EnD7LEokA8Pdlf49sy1oW2ZB8hz+ryUMbw31BnXO+78w5I7D+6rMwelCZdYYRNZRGNlbBdSg2t/Rg9XauZDNLznmcmMgAOWU8e/sa6/HalKLVOS95K/Tkw370xhau6Eo0g+18j0X7OGMMk4bqhqYabQeAc+44R78OADz988i1Fu9ZBN/XjXNb5Hy90Khzrl1xjWOMl4bIzctuxuwbZqO9h8+nK3d0YJ0YB6lKAKWUgBBO5Jx7PgtQV6ZhKNEcDiUl+QDS8gXg1ZuAV+zQ07Tis5DgU4rpWGcKA3u20iiV5jHgMVHyzssnOuXbewrwfWmslD+PU4cARq7rTeOuQhep1ggmpQNmRowzLsmo8IOcc9HOXG/ABdKZrYYzpBlVU8fpJymvetK6uwEAUyqfDHebY62idCkyrU0dunOo0olH4DnEgcc8FD0Gl5LgWVBC9Zxz8X+v4+C+WjG+lYi8TR+rPeZNZJkeMVf/XjMk/CyCGHtXhY6QWQZR5VbBESMj5yy13lCGQaxEzjmsXZnLVnGHfTYI0MT0+04jnx5K1mE5kXPVYeMVgAMvBOpHxR9visXJLnknqprCZ1vpFQLuDDPFJNqk+Ooz5rr61yuewV+/9kxyGyco/U/lnlKIYjFMKV0ao4cUch7u+91i7liMkfJyzvfD2k3Zb5wPlIiJmThRmFqSDBoWKr9JNVWt8iZFzmVU/ME/RiNAYVNiBpsNNu4XMGEuz3eLi2a5Xg4zl/0VFZUluqwtcq7Kg19HrGfXFJvXUBp9chE5QeQaT4thyI4R9Xc6cTnnZZZnS3FT/v8tM875RG99+pY2ZRjvEzJyLp0Yp7wqjHNFx6XVIu9MLHplo04MeXzFLnTm+PNPHYWzyNXHXB18fnH7i6nO+VhreyKUTsIsRzXac+1ac62RbbZIB23mTkPJLK8Tg/H/a3Z1iX39WCrMx0fCKOdfnuYR4yBybpZ4sYg5RZwyYxj2bO3Eokd5fmxXplW/XQwre5rIeVc+HIPv+s3T2j57+TZLnq0PLN2q5+TdsCiZzOnB2hprXWAfPnKFnA5r743O+2syIk0ghbNDlZe287KNV7/I+23O523Y2RE6PctdlgCgKwZ1EtQ5l5FzFl7fNM4d1Ti3NGLSkBqMFqRwqRwI/XTgVdbw8cI8n6cpqTwAhnOgvjH8bHK1eEUfmMWdi2gcnzjf9Oa9EJVXxrs9ZkozxjdVW1FvlPBnqlajkM2/7OQp1uslPd92sQTLfPHi4kXoXbgw2E8yGTDB0l0zhfPX1AzVneou60E1bQ3PMeevuR+Mvb9NqKePpblD5sYcydeaoueh6DNUZZwATUII0eYSR3nHXXJ+VObtcTW6w378rf9C/emnl9XuvogHMeXWGwEDVZ8Qjr9NSl8kAM7vCBEJVHQHx+fpFEwhqO0TW7tFVKNzcAfTS6mJnOhju3tE+/iz3eFFS79FrhvMw2UY56oh6uW5jlcO27t4906zwt0g0AgqMgQIHY0lnYgycGPR7Ve/sjOyLU7kXbpdBUk16uDwc0ZxHn9MWZti1v21r+3CukW78fwd3CGulvaUkoqtPYYTIiK9dmK9/2bZb5wPkASwWkJRjoObKJNlGqMgq+RqvVm1nYsimpS3KIVS4iPnyoANIucFnPbJWbjo6qO4VvDkNcEhxJi8ijtLTEi2yHlfxQrpkdhO8VKlx3HBVWVdmiqTnhNXo9mcsMZGCVXKkrfcOI+PnBOLye6CG6EZD+j4563oyXNDkYnfkVfRUdTI5duHqIOS0LMEGVFjhxUnyeRCwerxlznOEnIet7A7JOrtjuTLN44D7WkFAKza9bq4rpKfbNw/iYirlNjmsbsX8ZJV8qoyhcFs52HjoykQ8vdPHlqLJ756PH5wzixsUrg5Wqp3oEBjnHRqynmKqIoaOTelw5I/61r6sc8Yrntcj+q/vGlDyXtj7BGRTYz46Mh1as90nQWOTRpf5v+ZPrfkdiYbB9999rvmlQDoxuaiTa2J1whPDZ+ve3j0twBh36CUgFJ+H5nPXG7kvCrroKm2ItLeWFn/VOljEqR2sLwXV0TVMqiqc/DMxh/h2IlhFLi7Xe+brFAA3riNf6E8d72d+NjgRvseZQATa1w5sPbG6ixPHRDKfsu2EA1EXAqHEBQUg10+vkwMqqsYh9JrHIsextdNs2wawDkJiJsJdJXskGrQ2ho4Wf19EfjWnPVAyq2/bDj9ksY+j5zz3+c6NCjv6BAHlW4Ycbc+GeW6FQbpnNPYWF6b+ygHThiLy0eOjjr31ai0SA/oMIyv7+/ei4tbOEJIGueuB9wy+CgtncXm7C3u3o1l0w9A+7qYZ3vIx6PbFJ11xiaAWnRYaR7LdLZbOlegvYThl4aPISKqU6GY4zpEGU5paYyqfALSCZWp1d+F1INjx5G8pkAvaGl6gq18wxt7UrdN5iR1tihr1vSzws9qJZ3K0ikXsi/IceR5DOPnNKN2UBiBTxU5lw6fUjrpvg5UvQNkv3E+UMJk5JwmLzKR0xTve4yBq8rJhygepTct57x0t5HRdZOJXFsTZaSu2AsnQ1FTWA8sud0wdPVnsOHDRo3LyI0tSvnUM/hiVK7hnpRvIyeTMrzEqqiB0VjWe3NCOu6r3JM7//N9uufbxjiP6daDG+z997xnfez6wQ/hPMUjz0x0oqLy6INx48TDwPoi63Z39StyboOTlxLHYlgs+f5peO6Kk/h+sejFtctWJsgznRU1Q0AFHPB/nua1hFe2rDRPC6Q/xrmZo6zOATJFQRLCmZHzdyt1roNzxPkuJRjXVANKCZ65LcxNbeoahUen3GBtC0kBa//mmQcEn7sSjHNb3q1j8cTaXlOFUzo94LxVf8WoWh1W6RMfFaRCi3zacqVJtYDeG8Y5S1ExRJWC6DfdCc9h/qQSFTQA0Kx9HBR9FhrdRMDaZcQOTOs7WuTc8lCLHkNW9NNUkXMVmdAHdne5DjKfwc06KORV4zw8bkLlS8jQcO3JVBhrxk6diK5Y9NFKGV6oiCqkm/f2wGd67q3H4tegA6oe4fd0CPIiwh/5HRUOKCUawWTAoh8zSGKf76STkKniTlWTrX1iM0c2kEwmQBnsbC+gtUAihFQETIl+WsaqW4IY1hDZ2gumXYArDothEw/uRVAU/V6FtRNCUKHw0JTSguYMnqVfdwBI4H51wq+s2x+toFH9wWLgZCz9/tJWYZzLJdUHpjl1muO0Ix+NXudW8Yo6Wx72Y1SMaF8y12liyT0OYuCi3zPio6j2F4k6Ua97FK8EVJbOp0bJn/gxH8TlRM6lLqhUpWFFbgwTCgyaGqISgsh5yVJqMnKuvLvhc4Bhs+CXIIFTpSiQS9W1xjuYcY7l6NISvF9xOd9jcByiBZpS8eg9eiX/v/bx5OPKJFz+b5D9xvkASTDp8HBA6vNUhTsWGq6ImqPzZuWcp0njkcZ5JqtPbhrURU5mojQErj0sJFwrV16/DfheA3D/1/TtNAPkO4FfzQX+c3F513QthHBS5OxEKM/lsnmFE0RVkuJzzo3FlDjAd/cCp5YXpQ/Pf3ON88jCmxA5zxQ6cfTBdgPguDcE9LVLsM2K36GWwaZVQlkTClD3C+kg5KVk8eZWdOT67rWNq6ebJLby3jUVLqrEWHLSQuIUieQYZ6thIO3QU4ypT4t9C2tXmy2vGuScm/e1GBPSwI4jq/KJp5VkU0WDtcdMZAeMCPNsl23T4XSqsViZcYAnr0GFglS493NhhHh95YdwNH3dSqA2pGJcZJspq3t3YkvnFhwyLKwpz4jP6+OWwJZLxnpqGOckJm1gcuPk4HO3Al+95cWNJdsZRw7mp1i/PMaCd0JFnXMZOV8/dw42feYzqKsUua/iODfGOPcZC1INWrpTKHNqzuWm6HyxwfXwUJUdgVFdn8XxH5oGt8LBnBNGo31XD1a9FLLFs16Dp0Jxjg2faEammHEoAyNRwF0vYZjYXA1W0QgghLUXWfw6Naf6HgBA1qEoeL4VpcXJ+HjVBrNFccGvQmz/YxiZE6kqinpZf+YZAE9358Z59TDgqMvwdPso5KgTcXLzyLmi6MsPcz8ENIwpP3IuJtVDhh2CDx/w4cRDHeKExrlDAlg7NdRlAgBDZ+Lwnl4c2JsDDr5I2x8hhBsA49yWwhRIxDiPrq+bM1GUlWy1zDV3PH6tzzeEucgbO6LzgqzGwm9l6TgWw5uZbZJEbJkaYPCkoD0f61ZKrgG65WfRZ/ynf8M/tG+OtiNOtr0Wft7xBkez7LXzmFhFRs6VKHT1IXzurh/TA6osuE5K41yOC023X34PsOONVPaBKVVDDZLh9/8N+B53xuCSZ4Hzr095Jemw4jZLy7YurF20WyvlnArWLqUYn7sOoGzC5f8G2W+cD5QohHCVB/BITM3M0uQSat6al4IdWp3cJIRmoGWUqHM+PqHmuFQezSiBNmBTWPkj96Q0smR5tDWP6tvdSqAgjI7Vj8SfXzUoWi8zMXIu3hOhHJ5fJuxGNRJS55yXq5CYEhjn/cvHLiVMlP5gBsFMonFe7EGWJj9DuoNHCKXSphqxmREcPi4Nru4X941xnlY+ON2eA2nWwE4jd9Ymkx2VipxXudGo7MTGifqGTHUw+UvlM6mt+xLWrhqW8qrSmPZ83dBYuytKxqdGzr2Cj+XP6aXJaiqqY43zJBCTGgF+15wRmNhcgy7DMbNBYW3f0d4LLLgKP9ihEBIZDtKbsv8b40SJkm/FSaMwxgDhePAYmM8CQ6tpVLS/eETkOkaWePu9jh19bPB5T08Il2wvVR0D8dDMNQvDmtB+DJLF9xkcMWZ5zjmPnA/pbgEAdD35VPDKJJSWErtx7vksgGH/+tFV2r6nb1uFJU8Z7Peq89USCbyjJo8djv23fewnR2PI2Dp85lfHYei4KGmav+BHAIC51XfxDYoBYb5ukteZx32fJ3mYIAcPQJXrBORyVJjvnV4z4kSyxGcciqLH7JFzQmJh7XEOrMjzn3KafiIAn1FMbOXPnNbU8ig8A4jrcsftKd8HfKBAXaDYi2xdONYIGBizqKd+IZEs0xRnENdV2HCu3CeVUJNCCcWmFj7v7O3KB+NTzo+TM42YIte2M34MlzE+ms/Wo9iSDCy8cPK9+xJceWXHK/E7I4ZvdAx2JTgMakR/cHwAfhEfHnMyrthjr7oAAMRVoWwp2gNohHDaMZlKzSCbktefpa/2gSW3Ry7Lpp8d285Y6UgucVlKOh/lumdh06ZgW+W0aTjgyyNQMyyPquZQH5JzXknjPCHn3CuE50rOplIyZg63QQYNr8bGJQYsfthMYM77U11HJdwr9PJxy3wWQvtJurQxHCtKIw6bFX/ML2cDv5iZql3/TbLfOB8oUQjhSEUlaMZHxfDSuRxa5DwNbEX1nr1ZbO0uRUW1i7pmOyEVELbdNSLn1jyU5qnRbUKyflKZDEWSjCBp5CblIX9xEfAtg/UzyTiX1wqM8/Jg1KqTIjY3x7xmGUqJ/abiPgMYOfd7e7F8xkysOPAg9C5dqu+MYR6VddBZTzIresV9T/DjiIS3AY0fuAA184/cBy23S03WDUim0opq6NA+REtKuU7kwh6XC/+pGV8pfZNMVcDWnhfjI6m8UFzuaSoxmukprNAjPOlosUfO93RFI5cSaus6FC/ctRaP3rDMaGsmgMlHzk3w5l95ziycOH0oDhk/CBmHouD7QdRWikoC98+XuBKmkRhZnHQ+A6oyBkM3QmWzyJKdUg518NVDvgqA5+QzwdbuVvC2uFkLCZ14niQCYy/tmMv74TOvMSHYAJrqe3DV81ehIJTnNJwMcWkmjyzbiZ6CdA4RPLN6D256YQPGdoRR6LpK4dRTnTIxxrkb834XPbIJj9+8Qt+ozq+W8ppFAD//f/NifpEuQ8fVobo+XC/YqzcBAOqdHZF7Rcgq1yzQvjJhnBfNiDo41FjqCATyueltH5N9NfhMwfcVfR97uvJW49yhiMDaQyi39edGHTIKIerKhqMBAB4IPvUGZ17vXbKElxxjjDtpheGa8YvcOAdAaPh7zZzzwMHl5ctyUlcfJipkNITl0EqJQxys3dWBavRi1tKfwxepctLY+M/Kxbh9y3agajAwbj5cAEUiuRnC59J6623adUtGEvuQinXUyKPid5rRxm2LeJtTSkHMHa7HeNT7kSv1FM3X9d+nrvfW9BlbrW7zN0v4cqZaG5M9GXUNZtiWyQDf2IaWLyzEejeqF/mzPsA/ZMtg9R99aPpjLVJ9BNdDSIWBZDn+6wCAmmG8H9Ha2sDZXRrWLnPOLWztahpKOaTTI2rQsr0bd/9mUfxBl70OXL4kfr903sEglwzy7lM684cLNEaSbttaGr313yj7jfMBEo0QjlAO6UphPDPNOC894GqyivL6JuWcA3yhSmLElvkwZuTcjHJh0ARe8iMuekQIGt5zfukGxXmlK+pChdlLiALZSDBSwdqJIP0rL3KuTl51TTH5c+Y1+5jfrtyU/++Lcf6PDwK/Lb14+V2CtC2Xg1OvR5Tya3he8ND2B7Xtw3e8AACgxqK2vVJHOsh8NGmcEwaM+N73MPbPf077KxLlQ9c/j3+9vAlPrAwjfhv2dmNzSzzcW8o/lv8j+PyNw78RfDahkGnENcbCWbN1Ujm5AO7qyKG1O4/VOzs04rJDhhwLU+qzRnQvUw3ayKHV8wfPAAB8bObH4ttUDkTNEFNxKCq5uVMk5b6AW5s9s2CB7uXF3JIv+uhstYxplsBSnvAzJg+txZ8vOhSVGSdgBDfhuz+6jzsCDiPL8LMMr9urqRWWeeAL/3gV45trNIh+SyGMrsSVNZOiElH5xIPvM+xY3w5XGPy2Umry91MzHzkFG74a8Th+WjQikx90K25ZcQue28rruyfVvAaA6q5t8M0ImZB1SplC6cfa1ZHTnCh//9Th+OaZB6ChSkSM44xzxkJDPo2o86Bhhd5ak8OXT5+G+bPt5QpNqR1cicra8N4y6kuIz9Fbyr2aR+sGg0mG2dKZhw8gYxg40lwN1t0xh4nt+hzjknBMUFIEqIv1u7tFeyyRc8qZ8ov9yTmXyjV10e3WB+2S/CDIuKCEgMnIuWCxrizmURTrmmacMx3WHswhXjGSnw4Am1u6Mf6Ke/HcGiMaKI3mDGeTToNkooTCh4eL3btwWus/4a97ItiuyfDZAKGgMnIOndvD7zDQGI5xb8Nx2xeelGmDp8XvNPN0Nz4bMYIurLKk1wiDSfIRDi34QHUTkGvT5+fHfqSdtv173w+/2KbfnUs5n5C6vqls7R//eDh/uiJy3sDJz/JuNkDOEBB8aEgdkK3GsXefi7PHRHlJGHG4g6AclOCgCemPtUh2HG+rM9hwgAhnEnUZmmd2YNzfb04NaycJkfORUzkqpHFYtbamlhKVDDJWGscmlgcO0F5EX9+l80+FtydKoJOm7Pvv/Uu64/4LZL9xPlCiEMKBEPh5ipbHlqc9DUBpWPvQcXUgyqB9s9jaAT6mkuY96VgwjfOW7UYkXEadYyLP3PZN0U3jFt3GsSEpSLmMjzZjWJZwCYxzp0+RczWCcdjZE+0HRYzz/kbO+2Gcr7gP2B1PGBbeI/xdpvOmZj5nm59YuwgfaPpCeIqM0piRooiBxfdL49yL6RYbDrwQC46/Fu0PPGA/wCKMMTy7Zg/+57bF+H9/DiHxP7hnacJZdqlyqzCmbgwAXRm+aOZFqc53weuLy0VvyjBdmZcL+3WPr8GBVz6Mk3/+JL7wzzBaZi74VW4VqjMG1D1TBWcaZ2utplm41MXkQZMRJ3H53WnEhG1nKuMVZHNKsUVlH13OKza8LuqOm9IyekNs5HzPlk7rdlMyDkHBZ/jWHXq5yHsWc+fijdmr8R6Hl1bTumGMkrFsW7tm6D+2O8zti5D1GeJSN+Au8ImPnl0+etrz6BKOiaKFrC2InEe8EbwNx40+LvZ+i3aGERXf9ntEpFYaOqUUTEaodW3qzuvzm2oIqj1kXFMNPnVsOEe6MWztvh9vTFpFfe4NY7Rd6zN+WQ4pSokWQZKGJYHPnbzKcySUaLXOn+v8KApKOoffkkc9IzBdw4xw34qcV9mM9wBfWgZfOGAojeagU3jAxU9jcA2P6nsxyAleSi3cN2UYN2bjHkFkXM65ADjiUuCkb8OXJI+gcMUaxrp7eH6qiJyzQgGMMRyycwWGde0Vz0X7tVqUNrifX9CZpYX85Zn1AIAPXv88vn776xh/hV6qsEWpVV5KJFu7JHf0RVWLyLlOBiAELsIUK4mCOXzE4dF11uibzZ+9VPvOYhxYSdJQkYDGtOk7hg7hb7eUw61uQqXvB8Z5pccCQ/9AkeZS73lAr33+BWJ0w60LeSWenpbwOGVeqJk/X8k5r+L3FLrfGUoMqqFHqdceIyuWEFy343a0F0uTVSqNSX+sRYh0vphjRtFNh8zuQOXUqSXRb4HYcs6FeEUfFdUuquoy2LKiJbL/zRBCiDVy7qY1zqWOnVYn7W9q5ztI9hvnAyQaIVwZkgRrn9ys59Ed+q4Jes75mx05T7DOJVmFDXKpiczXtkyMVcU9AAEqpsTD3sPrxNxn0/PAnlX2fX0RWW9RJYTrQ865Vi81tpSaWQqln7nibwYhnGqcFw3PvYSfFXvQlNmk7OC/q/aYo7XDzehnGDknuPcQgkHDxlqbsKaRw8u2XHZ56mbHExyFcuTE+IV+SFWoMDjECWGhykqdqEgp4jKGax5YESzcppHgWAyQh5eGMGCzjJSV6C1TDSIWuryXsxLXve9g7jnPOjRd/liMmN2tYUg17vysHY5pkuEdN6WEImYZE13TNgel2UyprkvHnh/HCC6lWzGdnBKwdu289Z+ObCsY8NN5nu68cIiDthxXhG2IAMkSrr4iFsDa7TnnM5pmaFuHVg8NPt+15q7g89dvf1077qTpQyHjhDJ/t+j7OHP2cKz50ZmRtvG2OFZyOBnNlaI6gLIJ9UcpJValtuiXNqg1EiV1zbGsP2ptdwDRcoRqmxwCv20bsPw+AGE0m8LnXCaGA8aEfb7cxRmn5bwxzKORu/kAxg+uDvhlfZ8A9SPBPskju3OPHQoCDzkW1ix2SBEYegDufZ07lVbviUbN/vDEWlASolQ+euQ4fOVUHpGNY+lXx8aTK3fh/mV7gNP/F6gaFLC0+yCYvWcdAM7kTYhwv2ZcsGIR3c8/DwCoFWRQhR6VAM4P0AfH0UVK5LxgTTfr7A3H3T9UEkPxmO8SeojKpxAnlFIw5mMC4Wlu/hqO4IoY58LoooyhCBkJ5c/rmFHHRJzTxIicN19yiX49S2pFKWmuasZzH3wOdQIZoInN6Udd4ICzgSGcB8nG27b35Q787cc+sqI51Aew4RkAwJxcHvdv2oInN24BuqNVIqQwG6spgHyng7Z77g+PU3TWiokTlJzzKm0ubfKKcMVCMmNnApRfyMql/Ng9BbuOENNo/XtlA3B4GSTCQr8hZj+xBHlSR85lHr/KJ1XZCEw7C16BEzymqZ60ryXMOdftFDmvlR85T6mT9oFg950q+43zgRKVMKwM8RUPmWmcnzL5Ppw3/abgu5Oh6HhAgQi/STnnAFcEU8HasyVgZEHkXF+YLqr9KI5e/F2AMAy68MMYec1Pkq+z7O5U7e63rBAe+X4a58ueVeD9Mndr+X1ATonsmQpjPl3UL1YG2Dj3u7o0MpS2f+tELQEhnPGuiGhP1YghOOPikBHWLKslj2PEwbbpQ7SyNjbJZ2oS96tig0+bMqKhEk9segLvvuPdEYNKZdSmhAaKvGqcJ0Eqr9kZKjoOgCdX7QoWbsdwPJWKYqdKP8tUwxELXa63Da4FldEtcoHzfWCFVcV04jHGMHdMIxp7oyRcwxr0FI9zZkVhxe8/JITb2fxVR42ZH0sIZyPvsolLaWKfyCFUErS3WmIO9vOhs+GJTdyoenC9nuYxtVJ3SDjUQVs+3jjvEXWzP/Pb44Nt0jlhsrVLWLvpbPnAtA8En7Xyf8b9XtnYEhAI7urehc58Z0DEFtcvPSeLjs7Szh016p1JMM5dSqw5lp5v5xRQ+1/bzh5OVsmYPmcrhsz9gqG9RfAd7KoWinRS2ykBy+eAf35Q3FOJnGcqI3O5+fx95mJd7yHo2BOyFqtP4M7qHHwAlS4Ny5BtaEex4AXG8MiZI3Hpr49CZ1VInmTWQu/dYU/RoYQE/f3wCU0Bt8J6Je3g6MlhioPqzPzon1/EJTcvREdvQbSb/7aZoweBHMxTodzhwzkkX4mc+z06Q3OxO5yDVLb2X2SuVSLnOqzd8xl+9tAK3PLyJpiivneaZk4U4hAHPvOxifFx6It1M2KcC6cnRehUlGPDpW7U2DbmcUKpxpfS12o7tdla3HXeXThj/Bn6DhvDNaXcqSDaqfUOQcq16wnu6K0SvinqAyiGoevRRS9SRbywY4exxb7WrX+oGVu/o1SbUQNKjhNC8d1Kru/IZ+4Vg3e4ePjjGF8i5VO++nLKGEsIPQCgokE4gtIbg0QGH0xHtmXdT5tzbo2cZ2uA6kHwfU7w6KQgax02IVz3RkxKFyRIlCD4AK3ks0SEupkSer8U+X7T9v3+okffQbLfOB8okZ1XLd0TFyFVJNcVTuimcU79HCqy4WTmOBS9S0LShr7AovoqhJJUsHZq/OaqesPrTUW+tmHcbry3Fl47C2DttcccE+xbNv0A5NaUUeIiTuZ9tO/nasY5LRvW3tOh4LT+/Qlg53Ku2N39xXC7ec3+GtUDbJyvv/AjWP/+C4Lv1Uceoe03IweBiI5U8AvY2LQE2RpJ+GZGzkPjnLhOyVzdXMWg1G2Py5t978GhIegxhiufvxLr2tZFIjCeonxrRjjhtXWBZEjl6V1hFDHTPgUrd3QGudWRyHmp6GAa6zxbAyrSPfI7l1gj59vbSpQ3SSumcS7ad+b659C8exG8fAjJMxWNzpZoTvncMY0AgHFN1daSYvWVdch6MSR+KXU1lxItj/8LJ02JPTYpcn5L8Xj9+6dDPoDPLfgcAL3vAIBr1HB2iBP0qVgWeuiGqV8icm5yIags1s9ufVbZo9+vtbsA6nL00Dee/gaO/MeRKPossU8WsvV4ccUodLUlM7+v2B46a5gFuiyFEnvk3Gd2QjhzHb33usW47pLH9Jxc5R0sE2usTJ94eYyDP9T1JhrnhBL4ikHCxGeec14VUT4jQVji477Wb+KfPwhTak4+gKMZ1rkeVmZ9MHDyJemYWP3KTjzyl6WBsU4dAmSrMfuEEKJPzvyxfp+YeU6FtauBOPXoasXR7lmU6X++yA1kT/S5YfXVqK7hfdltbgYVqXDEzYAVi2E+rUUIC+ucDyad4Zzm6bD2Z9fsxm8WrLZeQ50HY0j3o/LUz7F873Kw6iVYz7hj0BdEYVHjnPdnl3HyOyDkj3CIE/IOyd9kQfdViEo+/OS+V9tprmqOoGGstaGpyw0+oVt4wpA8csSRYaekEvUh2u0DKCTnKedW6uhEJtP9DPHyobHJHWRq1FXh7hGReuxdy//7BTgifYMRhhMKJRzUwltCytF1app4WbHhs4Fx8wX5YDqkFaDoN+Z7tkTOaUpYuzXn3Oc8EkxUXzB1bAAYPT3UfX5/5Bfx3q+FwYNta8J0hDQVoWyisrVLNNLh754Q5pynsHXk+QBKl1KTkrAu/LfJfuN8gCTwdInOVz2sgKpxpZkyn/xnmNcbGTheHlQxcMwBEIERD6AQnkAWu98rcsiNGTU78SPT9QOpy2Hnb+hR1qCOjPjnNDaial7InNv5+BP2G2eqgZqhwGeejJQ2iciZP03er4rJKC+VORk5LzNfyczFh6wtvEdRNEzjs2Yo+iVy8X34O8CulUD33v5dz5DcMp01m1bqBhKxsKoC4fz8u9d+h88v+DwKTEZgDOUGAPE9bpw7bsSoMYWWURuzEOO5zSmKvc/CuuUFQ/FRIa8O1WHt8pxSZETDhXL2r86PAAB+eB/Pd88a47xUKqwJax9caZl3aofCEcpTnhC4JPpu+pFmrrdHPMK5J3OjQRrnH1n+EFRrmbFoFMC3REieWc1RBtvaelGwwG5H1Y+MEqEJScsie+eirZriVGG8A5UfQd5pVO2oyDywHbqDaHhtNGohlerv79qDezdthVuvl9x0qRsYz7H12x0SRGM9sBDWDorbzlZZlaUBli6y4dZGeVJoVp83il48S7oqquMZiNaw394eKmg9FSFHQm7tWr1Njj1yXvR8OJRgzugGTGwOUTOafs6ADa8Lx5o6v3btUQ8BALT1iPJbGYp2h1khwFIohWacFxlX6imKIufcNNT0i8lvktzv8coCJs9oxnbHxxNVYj6U/FbK+N66qjV4FhLeOvNohSBrKDf+fnAuj4pWVkbH+QcOHYO9XXls3MvXIBXBoBrkajlFm1EhT+uVQ4BSsJxgOs9yQi815zwp5Y9kKnW2djXnXHEkJtk2j6/YFYwJaZznkkhhAeDRkNTMD+D5doeWfKcUYd8IylJSJ6hxHYjFOT30i19EzXHcYdfftMSI88Av8gi0dpAbiZwP9jxce/K1gREZyWDySWkj1QgkdG6tAL62ATjis/bjPS9KckZpGO03dZ9iLpjTKaP4i+F7NXWFcDnvQypgywbuHPCLZRnn4fMzI+fRicMVfd9cqyPiWCLnwjj3fQZCiLUCgxopTxLb+pnqPDFPEQBrX+MEur7HAgdxbHlgU9Y8xv8vuCr5OCF7evjz6i14GH/FvfjOnRbOhP8S2W+cD5TIQScXIOaDdZjQn2SJlFLzCqBKLUlqDACzrvRACqHJE4tfZHBcgh1r27XtPR1m7U+Xe0fvucx+H2VyrZozJ/gcu5AVuvkkOWIucPBFSb8gmY3dlM+9FH4u5oE2UTOXSkK48rze0w434LpSWfYND6kqzfHRu1QiSsIg1w5ceyjwh2OTj++nRJxFjqPD9oWMv/EGTPjP7bj+dU6SJRda34JFJMwDIxQk42pla2ziWwzOOImDMPcqZb98n2FL5xatjcE+pS2UUDRXcQhohmZw8dyLcd7k83D+lISqAyd8C9OFIgvGlU9JPmaWMSuV/61GjOYPPwl/Oe0v4c5LnuXwxZnngwg21ly22gpr39VRQpFNKdJRMXJSIwA9iMgIBZiPrjWXo2v11yOl53q7og6WB5fweTRf9K1kaCMbRmB3zWZrW9Kmzpu/PWu8g2GkNfjsiMddm6mNjNkMPFzh/h3rKz/Ej3Uo/HyjdozsOyOKRYwtFuEM0R2YDnECBw+Lid9KBe32mhz+MniLBmsfWz8Wfn4QeredB1bgjprRtfFMvMaV9a9Efx8VToWInEdVCXN9MlELSf04o8z7e2+8UdvnxEbOeQS4MuNgrQLHVg35ojqeC8p6+dC3wu3GdSUqIGm2KRY89PiNwffnhINtZ2GKYGtPVoJzvp6C4xGGiioXn7zySPzlsqOC+/sei1RxCYxz0U4tkiYcgiNFugi15IJOHFKj5derKAgVyq6+480tPbj+ybXWeXOrREgQGtT6JhVZiDLnnK29WEiEsrb7jdjsHx58L2qR89A4TxrO29p6kFvFnd1jxTxx6PD05bKk7iHfnKNEnNW7uwj7RrG7G//83yIGPRUtQ2Xr7ySbRf2pvEZ8f5GPpnHu+UWgwjDSgsg5b7FHCGp8n88vkmi1W5/DfB/2ajYHXchT8p74CfbeeJO2a9erVUBFLVBpNxKZ74PljPUlCYFY7IXDZDSc/1cDPybKTl6myysdEItIrp3/AWUSkMlU1hKwdt8PzIJSlS5kNJ6pqAoZORewdptxnhYwYEOepZHn/sORq8W8F7C/93QUQqdj2oW2t5X/b1ln37/pRe3rp256DQ+8sQ27O3nf+dtzG8pq9ztJ9hvnAyWBQqBEgC0G3IPXv4Ebv81L0kRKDonI+fjZTTj783MFcVq4PxI5z7+5kfOkCcAr+la4TYRErlQOiTLGSUY5NilHJUW5lD7lrkw+BRg5D7jr88AN7xL36lvOuQo7Cq4DGHmQ5jX7GcqsFxGVYSKvuy2ap7dPxXCgEEo1plYp2aHNqFTgfaQ7ZKY2hRvnDjKZypKwdlZG6bm4RfKJFWFZNdUZZUbtTVj7r078Fa6cfyWG1QxDQ0UDrjzqSlRnqnHl3Usx/38fjd4oW42rd+3B+zeNB/O4oi7JmNwUOWWqqPPIT4//ASY2KtUAhs0ELnkGcLP422qOVlns+FbjfP2e7si2vggLInsk+O4VfLxy4OVoq58AgMHPDwMr1kci5/f8dlHkejNGhAqfrYwYdSh6M3Z+hr4S2yW9A0ndlXWyEeXShYeL3XsAcJgyJ/NT4M+MBca53GpGtZ2WDRhew515zOBhOGA+L7Mno6ZrMj7asr0arN0hDrrWfA2F1sNRaD0UPzvm9zht/Gmpfrffa5QpolEHsOf7kSg4gABuLcV89FLBMpEhAJBRfmfrP/6p7XMosTqGJSHci+v0yL7aDi0DoaCslyppomjn6TP5M5dRrger453fK17YKa5PAK+IJnc9AGBkdgnPOTf6hcnXsqRHfx81PkHWpRjXVINZo7hh5IOB+b7mtGc+dFg7wufsIhc4faXB3TAlamTlDISemh7RVBs6sKcM5VUjXEqQL/r44X3LMOWb98OUumpxDnFC49zNYPHmNh7NFpFzUsXDn3+bbuuLBL15NULOgHVPAtsXp167c0UfuVUcbj2s4CNLs5jUOCnVuQDQVtkCHyFk3SFO6OAGgJ0cKUYZw07Xxdq2tfjoje8CBTD0xoejhmZMWldA+lVmapwpEePcKyASOZalfZkPiBJwTqCqOsh3WiDYPtF/d3CDIk/Je+yH6Hr6aW1XQAgXp4t5HjoetayDNig+ABR7g8g5EWaLuuaaukJekGQ+0foJ+/XSSjmRc6mTloK1My+YU0rnnFtysn0vjJxTAseSHsIYw2vH/gc3zvsOAGDxrsXWyy99emvy/WOksoaPzYrqDKYeyufJGUePjMxBJaVLcO10xgQuDX6iIhxcfNPCflWPeadISeOcEFJGLYLIuacTQlYQQlYTQq6IOeb9hJClhJAlhJC/9/Vebz+RXjT5j4VQbUVWv7IT7bu4YrB3q57XI0nVDpg/EmNnNgF+Eb2bQqXThI6wwptonFOCpKRzCWvv2Fsil6SEgVVoV+6hwqKZMVmpok6OZ/3cfuEyifqCc5gPLL9H2Ub4AlTmwhrrsYwhKQru1R+hDtA0uf8R+JRijQTYogcxE61JCAeEkfNh9aNKwtpZGieNkLjIuUqGpi6knu/hqc1PoVV4fjXyIRE5P2/KeZHr/fmZddhqy+UmDmoYw5jeaM17M2pbStQyU3UVFhZfIcv2hmkIaWHOfZEAROSGEY+927rQ1jgZxUyNNo+YioZtnFw0f3zwudBrMc4TFu6+DqHEnGpx0QzNREIWGYTjefaoRlAK0GwIoS76xaAfy19uOkrcZXfhrAm87J2qhM49aQxIkOOnejGLGlu7nk5BMLf54PROCqMkHTG++8y35pwzn0WWB9OZe+nNCwEAXzvdSHUC4CBm3gd/FzZnmucz7d3LMak6qwoKqzd7XCEZbVUYvoXIuuryt7UmsIqNnsaNXg8Z4AdNGOxyx+dgdxPQvi1SMSSCIDPkiFwmMu59APleL5K7L2scy3lU/h+SWRNCr8X7rmrS0WL/rMnBNe4Tl6LwkSPG4eZPHo73zLOjLqTDJCud6ISi93XO9q/yjRDXBQrFAKr7RvNElJKiz4AbzgYAtOXSRfvUlCT4rOw57umxL+GvDXUoir7oUjcsywoAec6R0D77PQCAc+44B758lD6LovviYPwij3ZfR86LfgHo2gVMOkk5yBV6jAd0bMcDtTVYnxVOEOqg2B19RoQRu9FsM9jNQ3ry6NoRNXCZ54HW1kZPSNAHJbml5NFQnfOmqpDv7VtEOCJlGOe0jq+1NUccru+IpBt4IVt7CVh7kAoYyTl34HvcON+zNeqIZgx4vvA4uip4fnlLbzQoAgA9nX1D2848hjttK2pcJa2GBPNv6vWlVGrlyIO0r0Xozsb/Zkmj9T1PCLmVEHImKSPsQAhxAFwL4AwAMwB8kBAywzhmCoCvAziKMTYTwGWpW/52l4AQTjxiUjr7xXy6rTt7xDXEBr8IquSAOS6F09yMypmcnfVNhbWT5OC15/nWvJOIsr3phcgxcfOVmrOsReCfMJjcVWV0yLSYBopndcVGnhuVRuSipoXzqYCJFXnD/3w6sPTOFNcyvsuFJily3l/jHODkRAU7Y+++FmYjuLF4SG2wLCAaJQQAynz41AGNgbWrPA1+WcZ56cVcdXJ3Fjpx6aOX4rOP8py6Uo6CkiIZgS2zhAlrLyWez9C17lJcPvvHpQ8Wsq4tCis7UBCv9VfCyLnIs/OZAXkOf3PPjtLR+rljuCE0cUgNGobq9dsv+FYybDXtElZXGTUI40Qa51knG7FAXQWGXuFGy+B5zAsqdEhiOTP/P0/CfHIVvjnr2FEhlFkqRW4rasb/LnBsUdCIURKXjvS+qe+zbDWP9Y29DJ4fzTm33cNEhu0VbOg5i1GisbUb84gTU+fcbIe8nRqlfupfoZHM9q6PXEOVr53BnQYSNSGvPGJyNPo8ZjrfxiTkViiRFB6we0XifWyy3vVQYUDQfQLsWNeO3YqDvrergEdv4NwUcm3NZB2cN/ibOGvQD4N1TnY7nwF1TdwB+POGHmzKRFEPtlKNAHdWHDW5ORZF8rvHOdR1Zg93uqhQ/sEXXYRTZwzDhOYaHjkvFrFoPXdSeWLuy1THG2Zq33l8dWhoJA3ntlyrcgEGpw8O+VXZLIqiL0Yi5/LSyhg5ZJXQ+wiJ6GNxc09I+tV3QjjAEjmX0Ow1SoRaJYTr2mm2xH5dD302zldedhM2PtYM3wN6W5V5zfPgDrGUydwuSjcOiTrsKCSs3WKcDxQIuAxYe2bYMEy89x4M+/rX9R0yBVJKy3qllFoJ/LmwHzRHj1cIYe2UaPOBFBOZ0+PZdb7+cgMzFt6LUBI4i1MPtVIomGo9NlyAizNnRyu4/DdKmkc4FcAfAXwEwCpCyI8IISkKT+MwAKsZY2sZY3kA/wRwjnHMpwBcyxhrAQDGmDlbvGPFJITjdUSSzzGNlEWPcu97EFn0ixqUnTo8p4sKeNibSghXKnJeYFbGxjQ5LntX2EtgEVeZKNXJZ8sr+oGqMrorSmjERZxf2QBUNZZsU3BdxgKPOW+UhLV7fKbb+Bzwr9Is8JXVxqQvF7+knPN9IZkqYGUUijgQUoyUVwHw8Hcjm+IinbYyKEHOuZOxwtpVT/C+iJyrohoc1756LQBg8W4OF4uka5S6lglnG30wAGChH0U12CDDScIY4PeOxbwh8xOPs9bGVUTCWPsrQeRcgbWrEXKiGGLFntJOjlGN1SAE+PIp0zBqaqO2r3l0nbgmv1fjMN14T5sZ8uHDx2nf4wwWAMhDiZxbYO1SVm5rjRj5PvPhs5BcB2dcg5acHuW4oSGE8atcB74SKZaOj8pRHAIuc86raJS1Pg5KecncS6IbzdQSI3LOGLPmnAcKm0LKeN91i/HIX5dGb2F5KU7CYvn82r14cd1efPKGl4Ntvs+CnHMp8neqY01FcvlIVgwH1/ComUMJzlr3LLI9u/BgVR5nXjIncqzMT5Zzli8huKRvTrtXKoqRyHncE8kL9IiaTz8yuxQVtDtYV2TknDHgoz+cj0/+9riAxKzCKHsUNx/L7XGR9ZbuAt7Y0oaxBUHg19sW7Ks54nBUZBxO6plxwQoFtHXydyGN87jfd6c3X+MYKKY0xG7dqfRnRkDzJZzSYqI6rjs8bqPrwtv6GgBh/BaizkPVmfaxR+R4IemRjDbSrz6ISTjq2eYslRBu4/P6vo3PRo+HmAKsxnl6fbPY42DdAyGhLfO8ILrT/PnPoeE958NpbARe4rwzOOEb0XYw6cDmv0ufJwcokloOrB1AxaRJYclYKaYFfN3hyHq8H5VSO4Kcc2mcM8bfnZJzbhPTOM8V7fwxfh/LpC59ZqtoDtM4L8qOnB/04eT9xrMrwuGPYB8BI97OUnKWY1weZox9ENyY/n8AXiSEPEEIOTLh1FEA1KTWzWKbKlMBTCWEPEMIeZ4QcnqZ7X/7SpDHk96jF9ehJWwNvqdBgL1dO+C3tQW5W296nfOEAWLmnFc38EmucWhMiSNFevbYJ8TeNxRmxrQ55ybLen+EF3eP3kvmnJdhTNc0GmR00gudmHO+DySOeGMfiNOkezmtyklblKhL9vsqV+8bE9tOjRybqxiE3U2zsSO/C+35dqxq0eGi6vhgZYw9G8FUxfDb4VSH11eN8xe264gPGcWvzQ3SlOQ4eXVTq75h1ME4vPe3uMM/OnJsJm1ZEiHSKCmF/PrknE8m7r/1FTupWrkiFQVpkO/Z0oW23T3qAWVdryrrYN3/noWz5oyIdYoseP8CnHXV5EgkPa3SYLKzJ0XO5+RyqHQq8ak5n4oQf2UU46yjJwdCCIodYVSoyIrw27mi4zAA9SNw41KdAE0VNXJe31SJW1ZxY1wiEdzq9dpxUqF98Rsn4eLjeL5t3LxdnamObjSM89Nn6dEuBoai50cj5zIPWpnDOvb2YsXz2yMKoe2VZI0IfbElCst8ZFno/JORdJcSfOQI7liR41V1CM8+LoRkL+0+OXpji2TA8LlFt+MXT/4Giyu8IN9S/w3SOJcM3yJy/hGFKb8MjdJHdNyXGiWaQj5V1LyWkXOx+Ybn1mP8FfeitTucm2uyulFXCjJqI/+T8q7fPI07a0U5TUdf4ygB1u7uQsfKNWC5HLLLeJTUk850S9pfO6vGXlanOVg8pRKDzbEjpciU9CEfcEtV7+jmkfwxyrq1uLIC/6mrgUtcPnfcH83OtMHlCRhYId36LRGBVqRZGWJGznc6DmZPGIvnK5X30LqB6xXde6JM7nHtYwArWqLkqx6MPafxAIbuV18Nvuc7dEcYK3qBM6JqzlyM/OEP9bnZYhTL9y7ntM58GDGmlr6zT8RSYrRssZDiuT43lktFzoN0ED+0AWS7Ni7dix3r2q3nmVNNr1KqzFUqBfWVEE6m5TCfaZHz0DhPeaG5H0ref+O52tciKB5Ysl3TxcoNjLxTJFXOOSHki4SQlwF8BcDnATQD+DKA/uaIuwCmADgewAcBXE8IabS04dOEkJcJIS/v2rXL3P32lKAOtvjXMAqgyV64Na/agQOrXhJKiFfQPGXbv3Q53ywUlzezzjmIvZyNFN/jOecNwhj/wLcPwwe/ezjPnS8hfkwNy46HHw4+My3n3FjU1MVy5Dz0S7Sa1RY2UScbRs77Y0xL41xV7tV7OWUwyyeJhZCtlHxxwRdxa50dzaCKO3iQ9t2qbNicA+J1N1XqfSPr2+9ZyNbjuZ2cPf9Lj39J26cuNmUZ5xYPcnbQi6ge93+gWT7+kshbpHF+4cLv4d5r7eQrqtgi9TtgZ5bNJCjDNpGGCi2xQk4fFIUOqvL+Q9IyeieLXDylAbnqpR245zeLgv3Esri20vD52BjbpQSOS0Oaq5oxvnks3Ex69IQqJqQ4iRBusO/jpQ+/iIOGHmSJnBeVzz7AQgMOAHzfV3LOWUmYn0z1aBxWDTfroNfnSlcRBcy+YXZ4XWFUFwSaamh9JaYO40iIOFh7TSY63ojBDj/JSCPgkf+oQRekMlgMouXPbTfuwaW5NlwfHaON27797eDztGFRxIenRG9GD6rStqlGa6Yy7A+dfhOu33ETlnYrObkWyYgGNuS7UFsRUw5S/A8j5yLKN3JWeFAZ6wNDNEJdyjgfM12ZPw4VRFgj5vL2ibngXlEBYtNePQI8c2RoQJSaN0oheXY5IkJqXGfhRr729D71BABg1L23AAAqEiKwHigofCNyHpaqLFmKSgoDEigDRMN5+oE52+50XRSZeHcq6uxyjgKJlDDjDUttbAcG2D4upba4ko+nvyrIG7RvARbewD/f/QVMz+VxfJfSF2Jerd8jIq/v/k2qtrQuI9jwwdDw2rvSmFt8T4FUWW5q4UopEN04/9Prf+I7WAhrp+B6VOPQfaQvqfxCfZUxh0c2ySFUqs55gKqQur2cQ0rwJ5gGq1YFRblnkg6fRpgPLefc5L0oKWXqNkXmcmSg0uy7Fm21pka90yXNk3kOQD2AcxljZzHGbmeMFRljLwP4fcJ5WwCMUb6PFttU2QzgLsZYgTG2DsBKcGNdE8bYHxljhzDGDhliy1N5O0ok55xEvFmeoqAX8l6QY27KDFm31C8i4xJMWXUrjuy6B8UVvFxHEFHuZ85SOUIIEiMBG5fsxc717fjAtw7DRT8+ClW1WQweYTG2Dv5YZFPX9tIeXeIkKLFqua7+El1dthj4/EJ502j+oOOKEiDlRc4jIhUUlSBDVfTHRif4PkkZUG8pCzYtwJXNwnCW7JoWYYbByXLJZIBH1nIlQdZ8N6Oa7vA9kXOkeGJYted1z7FmnJfx7vOej+oJv0TliFvklYJ9Tg2HaSbpgT58TGvk/Aabl5d2gAytiyoPTTV2510aWPuoxhB1IBfLUhGwecOSHVefPjY9s3GSSD9aHFyWWCLnnQrfQL4nfly9fN/6frUtTuaMatS+lzJYsIfn25qRcxXW7lKGyizVoM5FVgxg7Q4QRO9scuvZt8IX5wY5xOJ7t6eTiYYR9rDd8jeUZAhWhfhaOa3qlDpvoLBZ5sTlz2/T2yr+71ZSUkzuBb8jnNOPnBR18Mrf5FISIVvSoq4KJ0WBVSLPavBk+6cTf4uKLpcODlPC7iEj53x9og4FZr1H3jxy3qTKZxLuG767J756PA4ePyj22LqmSl0hnnIK8L02QJRLNLuvSpY2c2QDLjg0VNVKR86T93tBn9OP27SX6zd/PkTnNhjUK+ZwS7f0QOHA1/psEU6gnKftyx4jpbIKg4fkpLnk3A8CDSYQVLlUvpA+zZAaBlgfxTTOXfE7eivj05d8AHTUweEGC88LAOx+TVy7cWzfGmdc1u/uDqLBRDXQDrqQ/6+OjnFJCJf1uH740IaHZKMBAC+Nvg8HDbsSAAKn7IhsXFpjSilFWJZGLM6bYU/xvPRS/Td4Nr5pnIf675HnRdfp6UeM0L7fuuLW4LOqx4w5wB4QKCWjpjXyaymRc0oJljzFUWD7gh7JRrIsCeHUOf2L/3wN1zxQPq/H213SGOffYoz9gDEWYBwJIe8DAMZYEuPQSwCmEEImEEKyAD4A4C7jmDvAo+YghDSDw9zXpm7921jsOef6QFz9UgjLa9/VoykOqtQ3C2NVlFAYs+VxDPai+bxvZuSc0qizwSZu1kFNQ4JGV4ZBO/SrXwk+63k9RkPaFObdvrCyq9IwGmiaFH8tmgHWPwPsWtY/orUg51xZ0P0iAAK8/2/A+27o+7VVsf0GxoCFN2o5grGS9BsNiJZv1jE1ZF7tHfjsT2dHuAk2NvKIhDs+HiUjjfO9vfriqUJmy8k5L3oMTuV2ZBo5FO/SEyaEO1lpo8bzPdRXNKS+n+1accytaWDtW1p7Am+5vE5JJVt5PueOOFpnIgYweZ/lnIewN5vYjHNPOZSWSYi3L2TWqDDadM1755SGwwdpKSZbu6d8ZqhwHTjZ0HnjMz+ANlIGYMX9+NTsT2nX+M7uPcD6pzEt2wQG/VkG8HWzWo9wLjV1h6XQpHMkSR+cN9R02DBUKbDng8fZ+/jvn1ijfQ9h7VEDZdtqfZ6xdQtilmFUyEBd4mMeWantLwYOKRo4IXxL5HyJUjrIY9wZ5iN5nsgo7YuLdIWwdqJdk1ICSOPHQqB1VN1fMNjdENnOoI/fcU01aLY49KS42eTfYDqXepXUm2nD67T9pfg34nLOpXgsebzu8XXHui/WJLc6qr/YjHMPNCiXlzQnE2XfVsfFHrfEepDhDs47U6DEMGxm4m7a1hlEwmuPPx4T7rwzvp2yXfuYEE6KN2g8UB/DsE8AWhMGvOKmub1LZXk8CnxD5Bv7QNd2PoaoG/aZTE30d9SO0NeWtWe9S9GR1XYToG4EkA+djdvzU3Bvy9fhMN5vxrXqz15C2hlhAdLH8ySyop9rx/homlnZYnmotWs5AqOkc0ky+XvxxnlFdTRQNWyCDqXf3hWilWYcFRruthSdNFJdJ+ZOxoK5nlCC6noRYCjHOh9yADAlmsJoc2YW4OCAEfUR22NL65tDcvxmSpqeayuB9nXLNk0YY0UAnwPwIIBlAP7FGFtCCLmSEPJucdiDAPYQQpYCeAzAVxlj8aGDd5LIziM8X5xATT9E1eO8oo+eDjv7ZaCc+sUQBmJR5NmbGDnnSAD7xKLWYS0pZRjnlTNCsn+iLrRJXoI4A62qDx5D2+LnZIEeYSDG1WpMIzbmU7/IJ+EZ5wDVffNwRsT2G7YuBO76HHD3F0ufn8TQarwHb08Kr7OlPRK2dvjIeLTAmMbxAIBZTbO07apH1S8r51xhwR5Vj8+dqJb34dd5bq19arrq+avAwOCwMpwBNuPcZ5g3tjGyPW0ptf97el1wHaB0tFdV5naueRh4wFrtst8SGOfErgz0Whhx1RnkrcgpU6P8dZWZ0m3ICrh3QuQ8Iw3pbJi+5PkefAGZdcCAijos2bNEu8aYQhH461kgP50c+nqDyLl9rvUov++cbceHv0mck/RbTh1vKEnER6WSGpCJCSvmjDk/QEskzO8yN/wjR4wHADx0+bEBS36VQd6lGudH7LwFt1d8D8fSMDVC9nmHQGFCFgq6gqbpaQ/nryLj/Y6VMM5dJZpoK+EGcRUAAVv7oi5e9otSEuatWp4FhYdGZxsoooqoSUJIEhA0tsoo2n0SIueAbnCX6upm6TVTPKZ30vqzz9b2T2nVuSw6M1VY+PH1GHPXC6i58kfoVHKhpXFuwtplmklSKSrFXgRNoZLs6eHX2htTj1yT6uaSh8ic89G/uw6V0+K5b0g2K47vH6GvaZzLn8wYA6adYW8jcUDc0OnT21LCWCMOkOXOi11v1GHj483o2plFRWMBoAzjT9ll9//7lv4p36naOf0iHy+ZEAn2cNuXsD53GOp6Qx2ooqCm14SOSpn207Kdzx+spg/M3o3jws8zzin//DKkFKw9ZPKXxrnMOQ/nwzQQ8s/M/Uzw+ZgLpuLcLx0EoO9rq+r4DNLWKMGs40YF21NLRa3dDtgeTQ/04KDCpZF0ltTpLe8giZ1lCSFnEEJ+A2AUIeTXyt9fAaSyqBhj9zHGpjLGJjHGfii2fYcxdpf4zBhjX2KMzWCMzWaM/XMf/Ka3hwTKvhg4hR7eARVvkLr2tu7ojoXDBguvX0TPhlYAQG7NmuiB/SCEW7ixJRVjtRTOjWYfEIsWbLJut0oJxk+iJoqpMGVtBUgyzo0uLknHismQ61TXAgLPJgArDCdJZhw9Eqc0/Jx/sT0HaZzvS7H9Buml7kzB55BgnJsTffu990YPOvHb+veqKFRTGuc1ldHIbUPragDAlcf9EABw4YwLtf16znkZsPZieF5TTQUKSsSPlSCbuWXFLfAYVyPTik3J932GeWMH4emvnaDBZ9OWUntpPXeG+Ckj56oyt9V1gdWPJhzddwnoNyixdr9SqkXahf68L/eTX0IR1VipyjqlUULSMWrMAWqd8w8fHoXBeswL+AoIAMz/vEbeA0DrVcHzk9FhCZE3HqJHovNJEFFO+C1mGTeAaWkVXrqlPywPZ4mcy8hNhUtRnXWCyPzUYXX44kk8q+29T/9Dv15PaKyfvInnvR5OlwXbpHPNcWjgWJGGW/vu/kVVMspCvavTjgYyc867fW5IUIeEa4RljndIEYR48KEbRR4BGqv1NJfWhDKDtsooWvuM/tGd5/3mqnO5czMu5cR6r5KwdsG+Ljpadtw4bX/VUXo0sitTiT88sQZuczPoKaeBKY31QeEQX1O+z3BeRKUbhbdG2qkMxUHRalMR+eif7GzlVpmczFMACM4VxymJupHGud/PUrimcZ6XDP35jpAF3RCfEO287S83Jt+kS+gIY45Avp3363y7C+YT1AzNoaqpYIXG71luQSN08GCGBmv3Crx82dAwEMNkfXPFXMn4oUNB6gsMDJ7hrNzZGiVjKykf+lf4ub9pkSWkZM53wNbOf1dx9y60b6zU2tWxp7Qum1GI7QglQSphWUa0InJNZz6UyHk4D5Vl9DtZwEY4mI32mYLgmzCN8f9C2zwxcr4VwMsAegG8ovzdBeC0gW/aO13CaBEAoHsX70Bm2S8hD/85LDFjRpfCyHkBXSt41MVvj7I09hXWfvX9y3H+dc/imgfT520kwdqfu93iOIiTBCO5dgzD6GNDh4UaLe9+6aXwwKSRqU78tcOAk4RxaCmJUlJsE7XK5pmmvrkiJ1w4HVOrnuJfbEyyIo1hn0ocrB1IB0UqI3KuSmakgNeav0d5P4EHVii4NjhzY9tqEOaBirJ6Zq3zPhPCKY4tlxKjTBvF6EFVWl63KYt3LUamkJ6EpmhxpPGSVASjB1VjikJ6lURGpspL6/lYkT62pPJfgJ7jvz6b4Uy+AyBh5JxYvfy2Vo4qqqRppVfez/7+RIyc0tjXJkZERR3UZJ3SnnlPGG0Jdc6H1UWjUh7zAkI4hwGoqItwAbjqveVSAA/r2tYFkXPTAPCozTgX+xKep2uOT+JryA1b+UKbFHMCqm+Z1zIiOu4xFumjcZDpwuZo5YAa9IbEYH54vhPA2vm2R/+2LHIuAIzI2rcDwEXzxyttCrfv6shZ+yMJDBIRLXY4jJRokfPos3BJHtRC9XbDV4/GEAPG3ro9fs2KK6mktFD71pXj77G+irdNfQ+mYn3bxUfiLx8Lqx6UgrUXJZRYLiti3ZanVdXppIJrGkcHkfyix8BAkG9bKq7lgMJHW0/47EaT3cH8+atH9WodqriKOjRnfek55ETKU5rm9aZw3NcOLXkIKxY0xEesyHWqn+RcpnH+4ybu+N5RNPqNUhWFEZLIeB+RbsE5MzQkE93+ciN692aDn2FbdrxcVHdid39BNFw1zvPcOCckYGwPRpYCUSeKw1y2n4EF6Tz9EhWl2N+0yBJSOnIu2dp5Z97w6S9gy7ODUezg601FjWtdU23VLbTrKqUV+yJyDnz98c145jYeMCGUKOVSy7iYk4lHjhoi+SZ8xkAyeyDjxP+Ftnm8cc4YW8QYuwHAJMbYDcrf7bIu+X6JlyCfxmQjzIYRsbiBcfT7JmvfCQWQ6wCYj8ZjOOlU9aGHRk/sA6y96PlBvuAbW1LkHAeN2kdw05j6iwAw5swMaof3Kqye4bPsfOyx8u/1lZXAsFmlj4sT26qjQnKf+WV511uqUDDkLK59v7jvPbfWWVM839ia8IrYPJzBZez9YdIjj2DCnXeIY+IdSDKfNiipYzHOCfPBiAMnlXGe/tnlFL4HhxL8e9W/lYZRzBs7qCQx25RH0vssbQaSz8K61b358DmlhbVLxnmZw5zWqAeAY7t7gMpG675DE4io0kgYOQcKvdH3b2tlVtnaV+9+f0SNDlZnXfSUKo9nq7YAwFXI3yrka1QUS4952N3D0yUoGOBkcfHci3Hl/CvDtijXI4wbKStaVuDdd7xbK62mim/B8YaR84RoY2S+8TXkhhczfn9xwVx0teWCNeHFO3hOeGtjhN81GKOez+AYfdSJ6evOkCiMmIEE9kwQOackmDbkNluf47/FTsB49fmz8b13h3mtrqH6FSyOtbDOOb95u6fAaaWCb5l7HeQjjPgAMNnCSF+M4aQBShvnm1t0A61TGOcZ0c93dsSvw4eMH4wTpoXGqDkczbKDIZxafHCkcc7v1bQ+DALsqmoAECKJir4PRgjcap4jnWOVGFLtYPGGENWVY25g1Czf3hHbbqdMO+3LGV727v3tKcLsihAQEMa0HHeAw9TTGOcBdLnMkpKmmMZ5UTzvDmY4hQZPCNsIEjr2hs+JXLPT5OaV6A9iQRNJB1XaZScn+qSqV/nFUKcKrH3+v1KDxivGeZBz7sem+ZQlZoWeAZSSOediPvbaeT/PbxCoVOLAyVDMmD8SLdu7IqetOnI+gNDZ+uD6B7FsT+iMDDhL+qjDb10ZNQGpZpyXEzmvsBvn0p75UEhmx41zhrZcO2onX4OKEXeU0+x3lCTB2iW241VCyGLz701q3ztXjGgkZzcnWi5NnDQO0+EchBDgr+/iX5b+BwCQ3xRCx5s//zkAwO7f/6HsZl59f2iQlSzrYLTJXEu62/PYuSEa0U+UeR+N3ycVQeE1VBe6QR/+cHjc+qdK3+d4QZPQn8k2Lue8r/Kvj4Sfixbo5UDA2m3KteyrXf2DtZtpFe6wYQCA7OhRcOrqrMeoIqNyEr5GLGU2ZNkt38vggwu/jR6d+FkjhCu6pcealBufD6PGrkOwZLea90vgOgSFEjVB3XwY6SpV69x2Lc8PI4mPLg/zktPC2s1rl4pwqVLl+8CEYyLbX/32KbjxE/2rFKBGzgs5m3FOcOERY7H+6rNw+iW8HNge6uOo93InZT911j6JGkmsqXDQ2as6Pi39ICB01H9fVsklbqwU/boYGjqe7+Hnola5AwBOFhVOBc6bcl7YFkWBCjmnxTiIgbUDwNa61WhtCgnQaIpoiRk5J4QhqxhfcZHz4dTFX7/2DF59iJNxbl8ez78hFTe1v0txGMOvHv9l5JyGM8+MbPuY+yDY41cH1wL4e3tBkIX97vFkBNcLnR+2bp85skH7njV+s3UeMAjhNAnqeFuMegItcv5UZQG/qS8Nwx89XXeYlSJNNNvcIfqzzB/f0hoa72MGW+rdK3LXoq3a9+s/eoj2vcgkZJZ/l5VVZP/bNvfI4NghPTwgMH8yZ+desHwnGACa4XDkN9ovhAMfFQjXnc8ULkfRY1i7K9mIdkvMG38TNd+78/r7LenSHXGg9jXv5/F/v/Rwy4/1sc/y+XSRc7HOsf6WUotR5zNOJaCSlX4grICswdqnvyty7trhRn+edKK4mQOzLn3IhxEdH7WjLH1aqshm5JwaxrkYU1W+Wt9eua9MoyAs4qzMVvVBf1LnpLfYOPf2cKTC9u9+V9/huGCMgVCC9l32+WJI1RAcPYqnkLy4/UW8/573B/vkT+zr2pq3OdkdEsxDZRn9rRuBba9FdUOpazoZ4Auv4rWD/xcQDtnuIndIuDWrxf3K/QVvf0nqeV8U/98F4GzL335JkgCLQ/UNqqIVozvXNOoGX7bK5Z1XuUxxe8i+2PTxjwMI652XIy9vUM4po4MTGiWE+8eVL+DW/325vAbMeDdwxKX2fe2i8p40KJVJnJRiXlXle23A8ZLsqh+j2BaJ7Y/xXKHkQ9lQDwNhnKvGQ4AiiH8mbTkDTZFgnBe26kobyWbhjtRLeiRFzmVU7vURTwAABg23KYm8rbtW59GQa0b70zr0UyUj3DEivVGpRoYcSvUIISMAK48RdNGjybwL5qLM86js+Zy2iP175kXZd4+Y2IStrT341h28tGIp4iZVpucLsFl4g2qyGiFYX0QttWKTQbkOuGJs1w3moZouwoLPSQt9TWMFDpg/InZ/X4UakfMDxzQG34fBMs8W7bD2A4jSDwIynxA2e/fau8N7MmhInOGCzEidAQKlikjjXKAltkWdhIz4GhRUTp+JJFqWnPM0xnmxg2/fuISjAJKUpUEj+Lj2GYv098o7/4WprVEIe9xi6T6pG+euQ7BVjNNVO7jxNmJSg/Vc/fphHzd9giPWvKF9L1gIT4PIuTBYtGh4AFu2a8Jqab0djo/emGE76aCQVdtM4di9KT6CDIRoGikSLScdeHe+Fs7dIxPSd2xiInSC8SpzUSWsXSIaKqKlUofV8W0/um+5RuS5PXcIHKK7PApwUfT9gGPDlM+ewKurOCVs3d8L583erjJyvTM1wCA9h7670I1aCxKe5QtAKli75BTqn4URRb1waahoAAYpJdAax2JBdRVmTxiLTZSFRv3kkyPnRlo0aLxos4N8p34/w5Y2LhTdGFxbdoyWDcDqRyJzqESjFP0QTVKdD3WnIHIOFomc9ykyXKnMF2+xce73xiBa3AyYz7vO7OPtTPxFv6jlmqsSwtrTP5+927rQ1RqPsKGEBOtmWY99l4jo7zFSVALjPAsM/v/sfXe8JEXV9lPVPXNz3pwzG1hYcoYlSJQgIKgvimCAF/TFjPETI2ZFVAwoigSRIBmRLDktywKbcw4355np7vr+qK7uqurqnp4bFlY5/PjtnY7V3RVOeM5zpmHTpLP8azP05sR3DnA6Jdxwz5AkWPs2/98Npv93XxP3UAncxv7viQf5HVaC3cb0Jz2HRFYUTeRUalmx0mSXBGd7aX1rAHcrJoQATl6dCPu7VfjUaZdHYVJGsYvk6grCMidsW2J+/XFfj983GBebMXI+8HePAy8K/zYZvcwdXli7MBYS3Kd9ekQ/xjg3ef0LmzZF69En3EtA1Nc2LcZvD7sCFTVRg6PFZ2d/8588sqwvLp5E7DayPT6nVGkSY6iU2I4tosN3Ldz12pZU1xISVxZRiJ5zHkT+TMa5gezprP3GRbbNGl2Db94bRvyLETfJQsCAvuHJVgo+UUxzKMLa1CPGV+Hf5QXcX5UP5kEBg2YeQ2cL748rXtyOfL8D5rFUbLWDkcqshUOmhXV3XTm2JiJJf3kvcM/lkch5Tjat/f7NrNDhNbYqdCxQQEHiiFxKmSA9LNnlGz4JZIUe8YIyQ0A6WLsp5zwjvV+HmdcHvYhI0jQrpoD1zb0ROHVt81bDGeoFt2cnR3bL40e8N+GEmDi3MXJ8RKSSSX159RvqTqV/r9qFjl51rSO+kigMidGZVVgvkp4TYO2AyheQtDpN2jvsg3qf7+tKJlbV89eFBKznJRiGq3eqjgA97UbMyTqsXaAkHIOT28TBIeTg/mcVdIEjwdp1mTeuFp8+jqdS6JHzj7eb0/Z0vobEN5GtilR6iQuKsFwuJaxdzSseqMTlju87cl/gsE/zH3ufA1ALV4yWy6f55004IHLuhtHh34whDBZQC9TW3hQRCKloG5gb3SgI5YITnvsl/zcIRKmpIrKc9dZnwttKbO3u6deo9x2IuhdLPDy0MoHsKopWLZvF+7JcqQgAQMPIuZgLVjctUg5xmBOdz33RY4Zp5NZvvYg/f/nZ2P2EkvQpDSbRqx0Jjg5LVNXwNzOGP730vH9T1982iPu+QyUJ1t5FCOk0/N9FCCkRu/xfKEFtX/8VU5v3rhSzhTHCJEbTpCOiu2wb1ogRqD///JKa+MSKnZFo4Oqd6fKtNi9vw66NXcj3xxvzkyVlIlHGzE/en+dtkutmJ5aNq5sUv29QkXPDcKEZ4H2lpxNErhdLCDfUxrlr/jtGIpGyOOM8pgxMRDnx3NgFT78XMZS08fzFJt/jE4Foj6CU8UuJ2Vr6zFbk/7YxKNWyfHsXXElRGlsrIvjp+06xI3W2dr02+TdPDxfjjAHef9TMkVj/g9Mi2+WZoxRYOwGAvvbUx5cixSLnjNCgrZRSvFjuoIeGBohw+i361wb89WvPY+XL2/HoDUvx5E3LwaQ8/eGSSq2GtCe/5feEueF47Sa/Q4b7szK7uUjP8UJEyOg+LeJpiHRYUm8KjHP/3/23GOrD+jKxYzZq2kLtmqaIlrBIz/WU91uIqa6h544nDT1xf1NpwjhHy46rfxC2oSJKxuVIsPaT5vFnPmgij7R5LiuuNMrsxzkHeO5aYBlHNbQ0qsiMK/62GJffoirCInK+tO89AIAV3sTwTRqM86zt94uGqaCZcI5MmrGm7x8+d6l9fuEsM4HZ0q1clZtUBMouS2VWndN1hI547s4lPLLd/SRHQon+VzDM6zLs3iMEXj93vtZb3ClqSW8mQ5zYknYP/N9R/lziBpHzAqXY1gBQQ2UQgPdHeUwkrhoGNNuZtWE6UJuUkZga1k5UdvuBihXDsdJQ3gDsez7wuWXAOX+M7Bewdrc7qvvtqJf6GQvbCkLR36o6zwOj3ABrN/ledi7mEerAuRHjHDYZ58p9JbZ2Ty8p+A622saipSjRqN3A+2zNie9RtjNYAOPvXKBo3hyjpne6njtkkXO9UkRNUxT9Il93QJLXcudd1TgX6B8CgrccrnNTm59TSqWpPUWSIuc1jLFaw/81jLEB1Cf475KAEE7knAdFZot3IqOCIqIuMfW5iW2XXCfzLQMBnFNiJ8/38RXQmEuadqDOO9u8vcaPDvpGG8tLz7fsQTDGsO2qq9DXqk1AerRWlsFEzjsNUR0ro0RdSpIdIUP/biulxjx4AH47YRZaA+hl/Hda2bZS3RBD4MdiysBElBPmmtMDAIyq1BRIk2MiGD/h4lLIuUGk2pX6r5fSjbt2Mc/raujjSvjy7V2Y3Riy0X7j9Nn4xnvnYiihU7rH/OYXeK6uUGDH1oXQ0iRit99esD9+cf4CANzAlyNgpRDCHdnbH9bqHmKRc85N0pWpjET5T993HDYv58r9vb9cDADYsrIdANC2jSsKXa39KOQ9WJnhhR7q7X7fAslY03kN1j8DuZ+UyyXN/HksUwgJP71H/h/m+U7Hka6rpu4YIufBMuKbEAUaDzMUIsZESw8/dlNrfHrGw+sf1raEeeHHzBoZC2vXc8fFNNvU/Ebk2CT2fb2HTL7pr4Z7Rc+XI+cnzhuD0WjFl185EnjlBniul0iYNnl+EzZ1hA7dLFzgX18HbuNlGu9Zsj1yzsodmlPF9xIu6jkHea8CnmxOBsZ5uEZedPoL+OTo8wEwEGl70gwjP8OO9aXFR+KM+XafBf3qs4s4yCU5ZtZI5beedsP8Obr9NT6vMqegtMEUOV/j549/4qip4L2Av4kqiztwLClNYBeri8D0AWDaCG4ZW5TAqtwQRM7zlg3KADumNnl7bwHPrQkdRYmzvKGCysJJC4O/5a7p5XNAivS7oSKEi5tfAxRY7ThjWFsY5y2/+718Md4k6fC+lrLwfGPlmoTIeSY+tcTr8PXQOF0nQY+856x7pMg5C5yWwX0Hu2QPM+FJnJMpEF9/Yt2twPWhgc589BahBDWN5bj8t8dhe+1a9dpefORcTLRpHUJP3qKSBcedt3Yx5y3asa6E+UmMS4ksm1/sSf6v79QuOKGTX+8R/cUIW/dASYqc1/r/Npr+331N3ENF9N0gck75RJFisCd6xYnmrSznHixi28EiKMv1T6/FX55bb7xUt8GgHuhcpsNdjv7ArPQnx02+Wq5e1cEhQ73XvgNuayva/3YbNj3ld8esn5MU4y0EEL7/acemb5+QFX7d7omHhtusTNFa7bGySlKEd5dxDuDV8jL8OtOPb5b790yAM9+07Kbg77/VVOPfza8bj4szzpExRM7Fwj5BrTigKxfGslt6PpoH/P6Kp/D3q3lpPScX7mcpHU1C6ZUhwNPrp8t38RmJ04+OYmaxDiP99v3cUWOqUZ4UAT9577E4a7/xqMhYcFwPeemZbUPEXZc36o/BG+s2YlahAEw9uujxAxHNnxIRQojyjEu/fRJ+cf6CCEwzYIL135HrMDg5F2WVQz9GTHLJMdPw0cOn4Bun8ooZeO/Poyk5G5+Pv4BfNjIvpeS4AEY6Lmbn8iCXvagcnvf4mCqTeUqCvE6+bW3T4qLt7vchz/UVfO1IijLo1Q9Awsj5tJFVKEQQPr4DQetrVVne9or+aHSceSxCwqVfT0j5nDmRI2QGfCFyznlv3sVE4hMqLvoLPJcphGlNI9WxRynBvW+eGfw+eNft6n7DuNfh+ITITkEKR+Z4NxDC2aSADMkDngsqkQZ6BLj0GHnukdohGcETZzdgxoHFy3kVE5GysN8kc1TZJJObVCdehLDS7685v768VcNjOddfyInjxjRqSjiAG55dDwAoz1ic4qOHOys3Ee78ykAeM9RIyhdkzxACm7KglFreIqAeYGmOJXH8/978Kv7n+nDs7UoyqE0VVKR1S4HSFwrRtC6TDBEhXFzkvFj5QxJaauHGau7okI3z3mZprpMDC+I6QeQ8eg/Wm8CJIPpPvZ6u4s/zLHyHFVQNKE2rmwbb5fOaQ/MR49x1PIUktmRJgS4cjCTxfwAS8nb9s8Dml4LtHf9+je+X+t6cxjl4atrfcNArV/vXdg0cIlxKzQ3fsqI9+Lt9R2+sg7VUpyEA4Nw/8X/1d/3ib/m/besBINBtLArAUqPs/UXSCPdESdLeBKXjq+D1zuVa5yWyfv0Xiu7ZdXJ8pnslhBXFQUpIgpefaZDtSX/g3k6SyRgj5999YJmShypLzpC3naamsEnyfeoCYMoXLkVqJ/dK7Ec+HDQbXrNzgyHKN9ovf5OUBz7+AKCiATjmSwNvnDyJ0IxhUYmRvjZgyd+Bp34E3K2R4MXVeRwG41x84W4AeODzuOqpL2L+VHMqgAzv/t6IRly+8W7zNWMj59q3YB6PnH9+BfCRe5Vdnj5mDPBH1+IKQlAeyX+Y1q18sn7k7tfCWxELblcyURIQdjOZPEvOOXeZy/Mqh6JMiy9xOZbPrxGEWjKUuXgEXLDJ5yVYfyrka+O08O9hojwN6tfHNohGCNgsQ/5aoFD4jyhSGIY751zIV06Zw0tshTTUpeUk+mM81xtG3j1C4BLCoevavCUMZVvpC2rOeU+WK6sb6s1zPD+J/9NUzefPhsr4uTmrVZ9orMoEjiKLEFz3+nX8Wj3jfM1dOJP48eJb5ymfn6euvx/ZHhVxxBiwZLM5/zfiqDU4mEa1vRbZJpAolBBkLAJH5PpvfY0b51IfaTPziAWSKaiKn5UiF1h2Gq7PcadjBNYuX0cc7zmRyLkemRYiPwO1KE64KEx9mTQvZQqZJqLOeSnyf8fPxB8vDBnadeM8cDwGhHD8W8wbx410eT77wlGXAwD2mcAjq47HwEDBOjgJ39TRfA3ISgiUZlYH12MRbgBZCKsOSqk52RwIA571VNi2cIQKJEkP42vLkrIEDhzDmkwrQ11Erq3O8oUwKp4kQ1TnPG6dKGqchzTr8kbDkdJ6vEpH2BQxzhOWzmD+1mvHi7nEDSHUM8ujlXmoXx3AIy48Q1nCLava428eJzVjReNKPzeleDRjRIBExLIiXFM7/sx1J715y0Y/j5puTqjpMheZOF1YqNYDWPOdghcbOR85Mep4KyrCwa3Ps3v5aXuTeHUH4VSmhr7ZE+vs3XMlCdb+Xv/fqYyxaf6/4v9pcee9K0K0nHOx4C++NThCz9MDACtDkyPntROVnxULFvD72LZCmJZGmqoMDL8lXSH+jMHkgVLbg13ucRgWoAza6f98KPjb03OkxAqQFDmvqAeuXA9MPrz0hh3uk6psfjncZmWMCqRR7v0/4K5PAE98D1h8s7rP0ehee1uBfO/Q55wjhMm6bj/w8vW4syZ+Qj1tWjSv2STpYe0eX1FqxkRg1J6W7WcqpdZbpeZ/6ouE3c0jLbbTC0YovL7iDOtCORjRE7KeygrNNYuu8Rmr1XvlmhfGXtPk5JIXwjg4m4Cil6qnZSwKx/OUqGiqtBJ58R6mKEGxyDnL1BrRATI7NSCXi+IvRxDFDVfO+RdP2gu3fFxj/H/xdyG7LKFR7Wje2UDTDOC0n3JmZ1n8F1HWfSL6tnwQAHBbTTVc+GNSi8Jfc+w1OGXKKWjIhCzFQd64/3v5qBfwyoSH8MisG4o+TxpCuAvmXKD8busNI8SUEjT3NWNcx0y8f8mVuPCV7/p7PIFoDaS7j78Xy81hytoblX3MY7j9FQMju0mkthZ2xJdnE89kU4rJTVVwJdWGR87j+4inj0Xtm9IUCiyR5q6dhZkY6dooD0K5hlJqYk3zHFCJl8ADi01HIYTgjCsWYPr+IzHr4NFKTfiF/7NX0Taa5L37RIkli0nGojh+zmjpt9rejj51LRCEtaL//Wtp+B3faprKCRencvSb63F+gPfvNx6EACNq1Mj5VwsfQztqUHA9tEZI+aQ2ogyW/10LNkdcLyJqu/TPeqPL+Rs2ty80P7iTM0fO5fvKxnmhkJxiJ9ptGZw3A5CBRs4Dtnb5ufy62qe9JCHRJOe1MRUxIIQzrH0GQrhAQs9eYjsBwCZRWvxvHPINfjphRmOzGDmrUT7xBHDyD0N2+qGSKSE/gUviiQ1lIVa0bJ3o7fIaHyHrZCwSOX9689PKeQPxx9sZGhvEO+QMbhqWVMKOxHz/sfvyf2t41RLh9DP1pLW7orXe93RJZVUQQs4mhPyMEPJTQshZw9ym/wiJ5Jwzh2tUklfe5DT75DXHwLIpLANDM7+u1oH96KLb3Y2uRx4tqY1zx0WpA0odrHEwlqTofzFhjACzTwlLrElGQ3bKFBBfE1xz0skAADenKT/DYNDGSikEGKI0nEkKEuHGi78HfjQVWPPYsETOBcGUq01zbVSULHGCRS5uwdfFizPO9UoCnhvrzIhAaiXDXlynvE+rxb7LXPaHeC4Yoeh98SXjflm2+rnMB2w5KdgmR863dG/hxrmmdHh5cw4jEFUIPI9hV3do6MSxIwsoeqkebZsSOC4rWos9InKkdJjy64pFzj27PAKLBoCGsapx29vB35/r556JdzwoEpoEufzYGTh8hvSNPQ946EvATefw39SKzjUisnbQx5W5HkDwfg+c3AS3nxtFr1aUwyWEg6C1qPWCUQvwo2N+pCzSYjxO6ORpQx518crEf8Kx4lNr/nzls8j1OZJxHv/MtVl9TfACw1e85pG+E6vC8Z16xItwQDXVckVx1uOPwtO+LfMY7lxkNs4jqS3S/OG2xoe8hbNLRPmzMsmannOutTUCe5Xb63mgaXhiVv4z+Lvb5YbmRNfvGya2djG/uAWuG0iSVGVh4pxGnPzJ+bA1ksLq+iIVT1JK+QD4G/TIua64W/VqvvGrcvlW8OcVRorjMjDCHaHEIoFBKIgVuxif712PYW+D/hJc0woh5nkboB7wf3l1fOnDoAALLiNodcaYL/rn0/h30+dmSZGz5E9cKBgdzBERc36aKGrSZWKivG4Rp6sYc+VzZkf2TWhBAMFy5Bp/06NpgcmR8/g+TUooh2Uh6mjYf9T+/tkekIm+Q6uEkqKB1I4FDr209POKydwwfSZLvXRIVcuK6P3M/9aKca45Yf7+Axd121Tk4GWPXaacNxASQsYYWFxwwZ+XSkLgBs5LqZ96Ll8/aSZYY2VHhtPznx8fLtprCSG/AXApgDcAvAngUkLIr4e7YXu8hAlQ/F/qZ65JpAf6wBg3sz5QYC/91UKc+dn9cPqn91Wvq3lXxeTvbNvG/y2h1vkbm6OGdZStN1n++bs3jdsHoy8zDyBj5oZRPe2Zy5ti8puF8jOY8mZJ8ty18ft86E2ibHk1fp9MtPbQF8O/h8E4FxVQHO0bfXYUN0T2++t+uOr5qwDAkGNqFrclzC0tmx0u8ubIudngd/W+LXUikdJgu8UJsACgkK3BlvHHoO+1KARWF12nOXHu6EhbMhYFiBThKtQGDM0mcR113/ceXIaDv/dY8DsfkwcnIsilLpkZi+dhlsxaKvevQUZu4kTUvo4zosv628x59dpL2LaGQ6F18r9hLkUbit73TJHzhP4t5ifdEeHAj5xbMXBzyXFnF9I/7ITZYR5xZ3NfYHMm9ZEySzPyiAdXIBQCR7PeBjdinAekbdRSjF0G4B+L0pcllI3znT/+SexxeilC2Xb1PBXWXlEJZEnoONHHKtY8Ef794m8xojLmey5/ELiqDrjpXJBNzwWb80xDTFCDcS7GWn87et3QyKQgJVVZOPfLB+KAkyenSu04ama8MxEA3vzWSVj0jfckHmOSjEWVFBp9XhxxGTcITHBUcb5wrrieBxCuG1FK4Pkq6sX2gwAAx4dWFzyW6JTr6C+g2gdNFSweOR+tIQt1BygFgweKTN8EXLd9Z/SiAjH3zM/U68QY1TxynsK5PQR1zrdeeSWsB5407isaOffnMKuuLrLvOx+gQbvaV0p6FbWRrdGuK9Dxk6O6UJJ/oGqenxb41t2J7eRtDS906fPXwPNYMKwY8eBRF+P3UvkTBhMoGjL5zJvAZS8A+34w2JShLF3knNL4yLk0Fes6CwC4Tz4X2SafN9BMtjjj2/LZ8uOMd6OI+dFzeZ37xbcCP5gEPHsNYIcpDUFAgxC43Rwp5Pj/HjQlPWfGniJpVvrjAJzEGLuBMXYDgFP9be9KkoheL5We4KUowsladHDqd2gdejdhr4Ywl8zPDVVIrgwTf8fd96RvosEEiKuVWaokwQiThJPmEQ7lMXnUkGT4C4VwmIzzJDnpe4M7v9ugDAAqhH6IRETOl5aV4fd1oWL4akU4Ed616i4AxRd2IRsvuhgAT7OYcustwXYzW3vKyLnSaN4XZqz5R+whpmiz09uP3OrVeHXBUehdvNh43uxDVaj8+IaKSLQha1MQyrW9vq3vR8/qr4Jk4yN5VCvp8sdn1im/v3632aklIK0L9zLnncaJbRE4nqfknKcSBdY+PJFzwbIeZ0QzQozRwmxFDEzTZ2YVOecDmWvssgGga167Sf1tyjlPKn8YGOfq5jDnPMY4lxxktL8dALC5dqX5WEnmHTU++HvDGy3B3P7pW+MdVlak7Qy//zdnAX55He/vIs89EOJGHCk7O3i/IrYFJhnnHhg2tapleZRLJUQPnebm2H16KcIyCWevw9oPPoLgwpEfxycu2oYJsxuikfMe6T7NK/GNUyTIuDzH/M1XtFc/AvkFuExbf4w55+Hfy/pUgzgpcq7L6Cm1OPQsM4FcGhlTF8751WV2pExaGrEtgms/uH/sfmH0mR7rkmOmcdSP/90dz4+cMwYn72HxUj4PnmM9AwBBuoLreoGyfti0aL49AcMV9/Jr1vZx49zTCeH8TybqtHPjnKAAC0f2ReHTsRJj4aSNnIuSoWwQztGOe+6F/UNzSddikXNhnJsIVNuqCarmGFIfiIV8l9ZXxPetrI8cnhg5F69oo29IXni/OCvaVi2nPN/nBHq0Rxhybg4nfXweKuuyOP6jnExyIHnVQy71E4FRc5TUpQxhsQg6RWw78gxMOEoJQWe+E935bv6dteM6+s3BusFEzrvbcxFVoWEMT1EUiF+3FD1E1vNvOge4+9KgfDIy4fzEv7OHLvs5lI0W6a28/XUD4M54p0sa43w1AJktaqK/7V1JEiYgl/xnWEotnFy6Wnw2U7E4JCmZteN5dFaewA3snvn161M30ZhznnIiK5ZTMuA8UOHTyNihkqspbKacJgDA1tfEAQO792CkapDMuVsXDU07Uoj89q5trE88Nm3kXEj9B84HrZCg5kls7ZokKRFCyWlsWx57zMMboiQ1z3fOx4Z/vowXDv0WFt9m5rG0s+o0+NrGdsUpkaEZZCwCIrz2Hl8IrIoNsW1p1CDZaUVAREtVkgWsPS4iHyuyI2uYIudC4iJdjFBjni21KJrGV6GyTp2nROTccwTcurTxfuHVR+DC7w+Ac2LVI+pvQqJzTVJOqiB4oxRg4TFxOecmET21p6w4Qkru12te25nKcRNJYyGM1/0GsL2TGyyd5dx4ba7k0HRCPBCxbujp25aFvKSYM6hKx5dOVnOlMwlVpplrgBQDAGMBsZIwbMst3TiXovfMQ5b2IztuJiybRiPntprqMaoyHCNWjANLzjmPNc67pZz5mLHmgqWqsjAQ0V/dodMah+S6WYvitH3G4jtnckJW23HQtroSZWOrUL1wYXCcPE5POesnOOWsn2BqU1UwdwFSdCzGSSOMc8djQbrFob5xvrVD5hcJH5Z63Dgn2gto6eEIPDFnUnhgIApfgZBPtbXHv4C4yLnjpIucB5HDoTMibclp2FuIOsNOKA8d0kFAxu+TtWecHuzzKFBzoMH5Y5jjhF5GDM/seQl9WtcxJh5iPg4I12BfnLwr6awM33r+W6ioyeKiHx6J+lHcYIxwSrydIiHVMjSdcU4oBfO0rJgg6MdwxK1H4Ii/HQHHc5S0CgCgMSguMRT7e9LpdxU14Zx27y8WRyLnH7rqUL+tA9C9Az3fMCdKSC7HY7DrFmFXechhstcYrmc9uiwmuLUHS+yIIYTcRwi5F0ANgGWEkCcJIU8AWOZve1eSREwY8kLLiNIBFz28EUDobaJJuTF+LmOx8lBVh8ZPbLqUnJ8qyZHvn5m4f8CRc6HI2baEvdEGbdxrEgRMJubz4ZY0UHodMppG5LJtQyQsBh1xUIFFjPFSjfOKeVxBa7zwQgAwsLXH1zl3mYszpp9hvnAKJWdL1xa0Vag1ibvtRnTmuff1tTZznpLIk8pZXIk5aFqFEsU/f6/zfbZ20Q9973Bf6LMkPuxs7lE8ylDMIx1nT8ZBP4vJjs4c1uzqLh2mthsI4YTELdyEebHRwpYtPejtUMez4xvnBb8UZKkKQXVDGcqrBuBp19+PEdYerYMciGRAsgI3jM7o6oZDCOcWT8GVsSuGi8Qkcv135jHkU5RqikbOpfJfVIW1e55vxBIvvpABYeiR1hkGNSX1vte3KYdPTsidZrmcGd3huRDLoh0Y59Juj0fOg28eNIeAWiQaOR8nRYEJUZjY4/LP5WeaUR6WFWWMhfPd7RdKD2O+zg6LlZxallb065YSoU8S8c6FQ/6wJW9i+yv1yG3rAa0MHbVxt7MtGsB7Cy7zJ0fzO3B9WLvrhZDgre3cKFfKKUkO/IZuoDIH0L42wInXDSgYXNAAOi/kpOrpuKTdzK0DAP1L1bJiIhWD5fNGQ1WXwGkxhM7R4+pCp5coySiL5LuSIuf8/o0f/nCwzyOGNRwwz3Hi+5qeOUlt1cvI2jEIInAHiiyLH9sUFj7QJiGxLpQEsR5ukcvuwUsXObcsgOnONX6dW1dwgmmPeXCZC6oXvDHA1TZ1bQr63Ev3rYvsN8m4mSpsXNZv9jk2JNIdUH6/mB87DDwkEqGixxiIVkKtpmJ35bTtfkl6sp8A+CmA/wfgFADfBHCV9Pe7kiBMyo8Q//LBZYDqxMDaFcn3cOVtCCdwkXv4kcMml3yu3FZTtD0VEYpBgmwAy471qMXaL+MW+BcZHniuIp96Fbj0mfB3Gii9KV+6GBtodWnw5qJywV3wZprzCse7DLe8+RdlWzHjfPu3v42d11wTbhCTqb9AR2HtLNYIybt5VOkM10JSKJKEEHRn27Cjen2wbdqYfqzYYii7J4mAYJW5/Li/7bwwYD6tzlTDE8ajv/gLkiLmcsWTOZWwPP6cmYwPUSyy5jIGtPbksdfXH8KrG0J4/EAV8+6cg+Xbu9BfKHF+2A0558WkqmebXBXaKPIcs+51Fd48XGztEdHfD5VSb4JjnGBbf7utnhJEznl7G10XZYz5kfN0353GQd8NYmfCtjEG5PUIsUH0yLmsawljLuP6BrQrxpUXdPi+bj5f1JTlUNe+GuSxq+BK451HzsPvNbJGNcY9F+ipHA2TeD09xrn9pw+/hU/cyFExwrmV9bXUfq8aO9d3gsLFtEntvA1v3s1P9BXXli0acZ9cOWPJ35XI6NieaN12fi25SgL/u1fAVk2oqLixRvi8MByid7GBOgJ1Ef1C9OuKfLjOdT4YVlcxIVxcxmBTEugidy7ajLwXj+AThnN3zsEbm9v965qO9HDPoX67hOOmm4SVFkzPAY/XqPdRLafvHIGFqz8IO6dVhRk1T/nZ/5ZWxlAY52lzzv1jelKQl6aVV3rD8oWO5+Bnr/wMy1qWBeu527om2B/WOY8GlL5w8JdQcxA39K2sNPaMkXP/Mrn4/ls/3cCqLYzz8QfEnhe2VR03ve25QNdm8HDOzHOkJvIGvaMi5wDw0QcAADZFqpxzt7kZPSt2KKkBInK+rjM0rl3PDfq6kMp8dHA4noPmvph5LK4NCagrmbi1vDqDUy/bBx/7yVGxx0dE9KV/fc2wL9SrHY8ZULNvj86yOyTWgmKMPZX0/+5s5B4pTDPOJ/p1QeecHjlURIYSjfPtS4C1TwbezdjbGmqdx4mYGL522pxgm5taSQzbKmpMK/sHqDAHJYTlyHnLKvUgbYCW1RX8MmfDrKQf93X+7wEXASNmAGPmh/vkkimlMFRJhBdGGWq2qxnHw42p8X53OUXHGpXxv6B7tTVpu+VWtFz32+A36+dRDFEeJmKce26sd6Uz34n2XDt+csxPcNOpan6v0XuvCQUFAQWTFGWLFdDek3yuafF2PRcEBDa14TLXN85FbXWflKj1SPRvPx35tsNAGX9OASVOk8v10roW5BwvyOnlJxY9LVFaSlXsFbb24V/ojr9wjvK7tXMJ9l76R7R0Jpe8YwxoGm8u+bfbCOF0w9AUOV/3b6DQA2fXLqz75yhsfKIpcr4wZlotC7fX1iBPCFKo8ACATGA8p3FWybdm2H9yfdFzZOOcMQsWZRhTy+eoqSOqcMDoA7BwDS+31uD4ZQstLxhD7Ts4+oQyBxmnB2T9k3CIBXgOXs060P3Tc8aqILyX+/bFiwf/P7iGyFx26lSjcX79v8MsO5EeUeZ/lrtbv43uthxo53rUbuI8GpV9fr5+x8aIoweAWjmj0AvW0x78/N3jZlI6mQTNYXxMPVyZ5+tpX3v0BGmsNdlq9GpsnbkCxWBFX9qHKnJOpPxXAEAh/XU9j5eOUyKIBLEQbwE5v/bx1fjD0/y9xTkz+rKGdiRUH8nAQQEWdoJHCcds+DRm7zoU2cq91QPLVZb46mOOUW/hr3nMcUoKUnQ/9ljxg1JKaz7khdjctRk3vHUDzrv/PHz+yc8DgMJ5LiLnrg/dl9t83JTjYdXWwK5wUTNBmqOpDaJjqH29rPuJJ4xtohkPYw9S+SpqJ/eGSMe6CcCIGH6HoK3qPacfMCpYay1qobZMIlf05wK31FSv4RZ/bstQlprVvNDcraEPfFSA9D4c5kQi58f8O5r+5HouLn7qIwCAyTMrsfr4E+Ds2hU5ThYvwTi3NH6dqfuMQHl1Cci0pAVcqnBkelfF+BT2ZEnD1n4oIeRlQkg3ISRPCHEJIfEYn3eFi8g59yc6kqngSskbt8eekkuT/1FkoomrN20S4a3OSJPxlrY+HPuTJ7GlPVlZlo1vwaIsS2dL8frSsjAG9DVnOI4Kfs55+ya+874rkk8m4Dn5R/jHjd478fABS8NU/m+lIVdPjpwzD7j5PKA7ecIDAGSSo7pJysRAJSk6WyMtiM19zSXD2q36ev8PP3Ku55zHwNrFfV7Z/gpOmnIS9h25r7JfZm3O5tqN93aYA8KI8nw724svErJxXl7gXmBukFughAaR88BrH+QLWyi0HQGwTBA5N5USiRtLl97EI2oyCaOce/2rD+2HH52zj/HcXRu7cMtVLyDXl46wL1ZkWPsgS/nEScPYKkyax8fM7MPG4n1fCGHDPfldyDh9eGFV8lhhLlPy3mQZUJ7bgEQbNx1bzIpF6zq4XbyETV+zFBkWkXPNCdtFKeyUTtFMCdFOuRznqMm1KLOLzyUKrN2zccr8UfjI4RxZNX0kR5FUFbiTJOOPA0q9iDPK7e7lcHCLwKUWpi7+AR6vLMAjqtLxuffMUs7b7vEyVswwR1QvXGiMOC8rv1hqP38/k8Ajhy0On7OZ62K/qrtx6vnVmFr2Ij/YBKMEgHxonBd6KbZ80RDRAYDqMMIv55w7jDszXPg51JVRwjL5OY6uvV7ZNamxyJowQJk+is9t1WX+XFXiuGn+3e+x5pRTY/ff4deun7YrjNpWH5fMHex6PMdeTrHzQGKhR26kUkCMk4EwWH6ffPBAH5noEQUppA8lGw4c2AFDvEhVsvV0tDLVOO954UX11sI4Txs5N0jPiy9h2ew5yK0uTu9UjCdINmCe2MQN54L08GL/tq/5/VyeAyyLp3ZQppG6EegOwmJTk4krKNeRCXPODXw0TGMpJwZ8vOAeoBaBJ5e18wMEuvP91uW34vGNjwMA/nzlM3j0BjUtYdjFn9tseKki50LkdyEI4WRdx/XcSM75ruPmQ5eOfAe29WxDd7Yd2LQWhS1b0PVochnmpMh5YjpuGklK55IcpdyBp76vtITFe6Kkeau/AvBBAKsAVAD4OIB3S6kVEVFeY8PSNk66QOILLwnm4E3Lol6u6HXNnqKymTP4/lIi5y4DJaqhfe3jq7GuuQe3vbQx8VxZIc4bDITK2vTwSwBoW12J9Y+ORNcWP5JsWbGRPOMiQCgw60Tgqg6z8TwUMu9s4NSfAEcbIs96zvmqh4HXblS3aYs6gOKl0oahZnsSK3pGmhJeWHl3ycY58cngiGi3KXJueCbRpg/O/mBkHwCQbPh+K/Va577k3BwIiOJN3tZljrbKIhsWH33l+wCAJzc9yQlWiAWPeRhVUx5CV00KYkLkfOGPzZEEIXJw5aunhpHl9+4zDucdNDFyvOt4eORPb6Ftey+2rOBzxsnzxmDW6OLPGr259H2GyQtNiAqxlsny8oz3LzsxKZE7O+KUv+Gqcx6REaohid7meK+/SWFu45E+3QdSSuS8z/9GGRLvdDpx8on43Xt+p6CbJs/nBuLBUxtxyNT4+VGBtTML9ZUW5o2rC86NlOthBNUeCcqtCfEcF4R5IG3r4BIKyz/Pg6p0pHEYTPn7bf5F3aIpS5bfFz7R9Rtl+66+8aDEw9RZdtiPxi4wX6QQOtN2vZlAryMRvMmre8GPnHvwFcppx+hnKmPNJmq603D5mr5+2lzcePHB2Ht8rX+f0m606+c/R35dfI6qIAxcWxuSjVUdrhIv6mkMjh85d6RBwQiJ/c56Pni8MFCP+/pXTODPablQ1p754+uUMzJwUVCuz3uqDW2slal9ouX3v1d+u608TYn196P/jTeKttSq4derPT1EVXY+wKHPva+YSUwVkd7d8Yu9CPFda3+0qohbF+YJ37hU1VOIbGxRCux4K2qcMy+K8jJ0pxGXXQZS5n/zMq4bVI0N00Zy7Rlgs/+M658GdkmEr14Bnva9dbZ2z2FSCilTHBFBzrm0Fvc7/fj+i9/HFU/wQE5PRx4rXlR5aoZd/AU/Q1lqpCqgLs8C1s4kh4fLosb5xMYoz07OT69kxA0cG8X0zKTUgEEj11IGoByfrV2WZa3LcMEhk94WDujhllSvlTG2GoDFGHP9cmonD2+z/gOEMRTsSjxw3Zt48LolfEDK/fsvIfHVRKkebaIcfEls5HzyX//Kb1tK5NzzYPsTsfCmiyhfMXisrPhFCHUAjJxUGmdgvpMvgKI8B7EzUaIQIfpAZNg92FZKgYM/oZR3CPcZjOzHvq3+rjYwuhcjrxuGyHmScV4l3Y+8/EcUvAJoumkCAGBV+waigLVbeuTcM34rofRHCam4TPzVrwLlJY7QrtwqB7TIeRrR4VIV+RqsbucRi6xbgfI3x6OpKovRE58HABBLjYQ7vVMjkXP5FRcjXlzXHHqH6yuLO7WeunUF2rbzcwo5F/09BdgWQUv3AHJVlcj58BjnzGPKJ5cXUs//EZdzPnFOQ3CNOD1mt+Wcy5BLIJ4/IU7R+RdPi+nVeAEKpEjO+cKvAuP2Ayoa0e47ywrM/K3Jup/gpwt/isPHHY4yiWVcRNcsQkKFzCBU+lAMFA5zcMyskXjuy8fh+DmjAy4GIYduOB0X75iAF+9dq2wnVdVBlMuhFmwWGufpvlZ4VPneHAnFHAe4enzcCQBSQLXlgTluPxz2PgMTtQxrj3lVY+vUNaDdCdsVRM6Jb5yPmc8dO5Ok+s/SWJPzaC1Khs3ZVJ6xcPSskQEb/EBh7V6OK/cvfvV43HFp+ExXnTEXALCpRkIUZFXDduEslUPF83POBaydEB7xlKPBcsTQxKRu7s4MlAEu5XXOAYC6BN+4+w0cfjWHj+tkXDZxg3xzv/UAAKtfQz7NPs10Q6O47e2pjiPZLDJjJK4Fv5+6nV1Fz+195dXg70se8nDYsuLrn5MUwJDmL2JZQKYSJEJ3ZFhltQ0NH/kwRv7fpzmRIwDmVzmpHBXOXZWjJcdUf0fEKVNgaooH0WDtruOFpxBVtzHlnF/72rV6q3e/+DpWhjCM612pIHWSREURRGHtBa8QZWsHgU1UHeySRy4BAHjwgrWASE5Sr68PbbfeqozBJPj9oCmeUgagPMaMi4dr71DQv/8pkuaJegkhWQCLCSE/IoR8NuV5/90i6nUCaNvW63vxpJ617inYhE/6YhJZsHAk0LpWvxIXagPZqtjIOclypd6RFgOnCATecRky/gL990sOU/a19SYr+nLkfMTEqCFulcAqzIUPfs+RYO0CDphVI4I6PIp5ANa9zTQIaRQq07czlDlRZDdHzp/OhdGgr9tdeGX7K6jLpne0hEy13Ihou/lm9YAYWLtY6iOlnHzJTpmCMV/nsLtsodt4zC8W/QIUVPEmpxEdkludrw/+nrf2GNQsmo61r+1CF+XEP8RSv5nXNxlTcBYAKXJegkd82bbSsoREtBwAHr1hKf74+adhU1J6vjmgpWMMk3HO1PlC/F1RaIfrL6qX/MHMgzB57xEAOEN7nPd+t8DaH/8usNaAgDA5BYmVyA2S0drbTynspO6y8Ergk0/694rCGWWRDa7qhjBKKd4diUcMR4VZgdNsXD1XkuW5wwXDgm3HAwAK/fy4siof0gsCSlygrM6PnPPzPLBUysOOUQcGfwc5sAWpfxsiQkBxg/PSGyXSLSsTKdMHANi2OPjTzZtbO6pWNc63FULHzbK+E/i5kEolVY+Gsv6z0E0hRwOt3RACEs6sgTq1PD9lY3RtOQ6cEhp5jVWiv4UdrFjOdX1lFh4DNrbyOXXWqBpUZGwl51yOnt7z6WNQU64aG6Ic3z8/I5NQMVieb5z7h8/vzeHFNTuxtYNHbguafvQ+61lknQbUiNQ63xiymLYmTTgo8ZnK5sxJ3G8USgO0JRd+710/+1nRU9tuuUX5XWHgnZWFMYZXOlbF7o9Ezq1sqsh5obdI9LWHcxM1zQ7Xb0IYUBdFhwlxmfqtdfwpN879uY3GGefhNh0l8LaIr9PVohtf23wJ8P2xRU7gYiqlJq8DHvMwf736fggIGivMjhhGJMeGpGfu/OnPsP1b31a4AxI5dAZbRz4NmTLMsHaAlyYtBYGwp0iadfLD/nGfAtADXuf8nMQz3hWAecFEwsup0Ei/cpiAAPskNq//HvjlfubreX6JHifZOJfJubZ19BuPFeJ6LFBmdKWmWARPPtotRI29gZZS61jH8+36lrwB7HUK3yhyyX3R85DyXRmwhBIp7xgxGT/FvKbDgAhIMs4fyYUQL4cQrO9cj/IU9Zd1afnjH/kf+qTpeUaHg4CjkaS4mh+Fn7buvvhjGAGDh3EnmRUF0yKje4VdqZZq1ie9cqRoJ9PrGAMYDV7yzg7Y2vk1U5VKGQKxtbyv/SbVpztxd0XOJaMjW25j3+wbOHjbLXD9/l3ZZ3a4iHnkhi89E5tfv1si5//+MbDiQW0jMzvlCMXGCz8aeymTAemmeYT+dqxrXIJtNWvRM9+cdmTFGEOrXuZ1YGlCelVEGI3k9Mm/W+349YWBKJFzyx/fjPCa08VkxV5aeotlga2XqmMcfAl2Iqp0ijzqVtvM+N7SJZNaZWKj1M3LqrH52QZ0bzGTs72+qV35PaciSublQSJYpbZaz9lzIRQCKs03uyMA9PI6DnMuJefc6w+/dVw515U7uNGek0gmdQZy/XWfu/8ELN7Ujg0tvegvuBzernmQPNk4NiDUpo6owvofnIbZY6S0McKNc4+EkfOKPIElcwNIzj5Rouuu1h/g0s5ybJpwGoSWY9doxmORVDR71AAqrFCqkuANo5OmzynCByR1QmJZgB01zpnn8sFsEKuJB1XabvxrdGf1aBACjDmiAy0NvhMjIW9Yh7Uv6zte+e06LFhrKaWKbhPknO+mNTi1+AGIT/ZdX+RAVZT3b4icA8CEZvVZaYI+5REveDdy5Nxt485/4UwB+DucoCF860bxubG6oQipcTFJKJ0nS5wuRX3kTSkBkT1Bii4FjLEN4OvMFAB3AfiyD3N/VxKEeV5gweZ6HYSl1KIihk9bnwZv3/IqcFUd0LIGAON1ziXv6qgvfjE81kA8khRFOOT7j+LPz60PlHrdi3zwFLO3TYj8LI7BOC8ZmucfLiYgp3lXLKS7dycfzI1zwkWG7Qm8EKYOUCxaOQyR81InsdrMAHKZnZgPEgNr94Ia0PHPKxYQmpAKQBmPnDfsb35G09YdXTvUY4iL/Uftj72b9saEbbxsjuyAYoUod4CopWpnRM45366PqyERw9jSFW2RplJUdkOdc8ZY5JNPsdajgvViesdW80m+yFHxvq4YKPfbjeOaeCgw8ZDwN7UCBQeI+jxMU+PdNSnGmOcgb/fhnr2vwQcOPjuyu5KMQhw3z8a3WtC+oxeEIBHWDgCjKvz0G2ZFYOxyTmfGwL0gBhhjCOqDu8SCHZNznkqcHO8HbZvCbdTCKIS5tMIRINa8V+t49HpsRi1zZcnKLKWxTuRdr9eia1N61nSLROc7AWtv6c6BUVs1QqT3uLsj532+o7GUe3XJTOIxzl0x1+2oDPWY+nPUOI7ufJUdaz05h9unlACeF6QcKAaasYRX9DmIyDmnwHi/atSO12qVOtl5aW6uhVpxZsnBPwV8IjhKy4DPSbnQRYxzmi3dmU0ICXLHnV274LZF88TjpHrhQuX3QSuTx7fOcH3g6AOV3wragVpAeT2PLUmn9by2Mv76LQlluo78LADgNftDeH3fT6GtfKp64VFzlcM9DbVQYOq71WHt8rMFbO2Fd5jR5i9Y1cxQVs4g9R/8AEjWVpUXQ+QcABZPU8dC1x13oabbPF4ZYaE/SPnm/t9yaokHZMrUb7H/SZNx7pUHYvxeKdNy42SQkXMxheyuQMjukjRs7acBWAPgl+DkcKsJIacMd8P2eGFQF5IEQ7mrleOQVvYvVHe85kOCVz7sX8MCJLgkrQ3hxmKByowPc9/KJGi57j3c0Znzm8XP69PyIIt1dNnA27KyLeHIlKJp2MTOhNs0ZcDN8fdats+h0gl7ACOEKTJZjG1yGHLOSy0/UUbTeTZpbWi0Vh9/vPkg5pqNc19potq+qf+4C5Nu+BMA34sPM2Mrvzbfy8DgwkVja5SF1eSY+PfmpyPbWvpbYEtKWL7fxVicCABw+wyQWlHORYO1m4zzQ6cNjrDQFCnWHXEXHzE15cWGn63d81iUfMr1QGwLR215PfFc+VnjoHW7j609cmf+z8ceBua/X9qsMQ6P8dFQB37MP2vw7a00VHkgsCMGV7Y8bMvfr34ZJMFJLOTus+5G96qvgpY146F1DynRcpkQzjIY5wI+z0D9XGrmR84FrB1oqMjiK6fMxu8/nFzXOOCWePRbPOrcvVN6WIKfFMJ3bmnGuR2T2qIbYUOV391ob4ps8wA0d+VxwHcfxermfnWu91xgJI8cqpHz4e/LggitLJPeTSJXy4ibJ8b7qQ9U6mC0Qo2qJb1uBgSRcwYWpAipxrlqpMS/Lm6cuxTI+q8935lRIudwHXzQegwZOMhoRGMZyWlje1lARo/pPCqaBARopQilYP4YWXXU0eh6JJk9W5ZtX/mK8nv/tQwLRi6IPV4ndWyq0KoJSIEeYlGgshE04ykpHm6HIRc+CfkMgucO/TZWrudrXyfhUG4nWxXqRZVNwOTDtfOSCeHcghvotoww3LnqTjy6gb87sRY7heFxOg9YTAGI7W/GH15WzsEkSp1zETnXIuWGb/Cxp8z91SNuWPFZDu4FgTIpIMFYZJ1d8cJ2jJ5qIDkuVVIiM12PQS+jDADwHaP/adD2NLPzTwEcyxhbyBg7BsCxAH4+vM36DxDPUxZ+QimHAdVPjhy6ayOf6A6q+pu6Q5wvIoXUVnPODZ2xsEWqCyhHt2MUWxFxmzFSjdwUi650tYQwt2XPbks8No2Qgz6m/rbt0IMXl2dfHi78aepKv+1SbpjIzvlj8jnDETk3rKIH9cVDVO2UxsSI//3f4O+qI/giW//+96sHFWFr18nnyufMQdVhPh+CMM5jYfnEj5x7cJiDbN6Qy62dyhgD1YyMcZ0zsaFzg/KemMfQ1e8GpDa6CH9HCGvnv59aGWWWj2OoPmByOg+0ScGV85g/fuRUHDvbQD5oEgXWPjzwEw6WUBvNXN4PfrVvkQwp6bS3Lec8di6UrQRJAdL7N7GBTBWQGbr61WV6eSd+o4hx9z/fDrlECv0uCIojZ2qyNSgj9eF5EhxbduzVGMZCIefCcz3O0enfxyE0iJwzAPuMr8Mlx0zHifPGJLZD5FVi1zKAMBVOTShe9MLcXt0478vxNntQFdPfZ1XVZcMbap3z/avuSGwTb1g0gvND9334RZ0KF6YAdnXzeXVbt6OuY8wFyuv841RCuOGWuWP5OtSdS2+0yNHUuLVWzF9Ump9Ful3wO+HxPI9x1n9CAcaCEk2dWyVHFLVQkIyGWMZ54sFi3Dh/bg4/ZuT8TsU4P9l9HFdn/oiPWw8qEXUA2NzWF+SasKdGa4EWzdihFFWHh+OMlJVWqQbgefy9Wkm2gcqaMcD1J12PXx//a1yx/xWR/Rc8dIHyuzZbGz8nUArMOgXUZmBy7k2VAbrv7x515ZUAgCl/uzXY5dEM+sub8MQz3BEg/P2Ns7rC4ItbACz13XmaPkDgodYK9c1Czgva3uPw1KhvP8+JeC2LI2Oc/PA4nQcsJqjXb4+IP9y2wDwvMM7r58rcI+qz6XXOAWDGC5vxnSO+E9nO4MHx01Xa77orvJ8YU1KX8FwWWVtEBZBBC7WAfc4vepjLGMpGPB7d4Ts3XY9zfHT0llZh6J0qaYzzLg3GvhZAcQrJ/3ZhHpg8oQelD2hE2RM1BOvtLVDFHwxCOaK2ytZexB6VJ9y4SHiQc67B+za0JOdCt2xNB8lJLZr3rO7MM/kfxIqF0bndkjIUk/8kpOuxx9D8hz8MqolF5eKHgZknxu/Xo+B2RbQ8k5DxB5jPGQIx5ZxPSyjBZ/eaYWq9r72m/JaJZIIJXk+iXPMYT9eIaRNNSLoUCmKccU782qsMDG39beisiTrCdCWE10ZX73nUOu5QaM+1I5fl4+Av237PHyWmnxFPg7X79/nULa9Fjl21wzx9lqUkUTQZo7JSX5KfajeUUvNcLwofdl0Qy0JzRV3iubLurZfrElJqSaiSRZT60UUuxWWY6wPZ9Arfr/XbvfrCdo/NJr8HXWRUx3mzzhM3jhh35dWqAU1J0WUDANAvpXHIkX6XucjVmPkBhOT7XCXn3KUWKtw8qvJ9qCy3UZlJOaeVCecr4f1Ag3XKzN1N4I64qix/L1vbeBuZCXovSU+nmipxyFhV+bPKwzHR+NGP+q1hqIFqiLsR887f7m8c520HdkpIHol7Q46c7w5Yu6jIct/rySklisjzcsz8K/qezGqgG+dJ4ni8tNTILWvQ8++nA2Ng68uSEUBtpW/Gjf1j59QFOed5W5zKlK9Ux3ifqSM9akQdwIiaMhDPf+b2Mu5ck9ogiz1ihIJYpGUDy8HNrYyHiidJ9QkqSu2R/SjKrDIcPeFojKqMOmk3dG5QflNC0XazRConrZEkkwGoHSWSbAjX1lFf/AI/dtIBwMGXoOmij2LO8mWoWLAgvKSI9DJhVPoIl4wXOq3cvOosrh0PTyOEo8RDpxsSqHXs7A2cRY5flrMtFyI57QyNjZy/bQGdUgMu1AJzWTDsKsdZxlJqgDmwDABzm+ZGtnnEQw8MDvkAsaoGJ3S9Y+y00tasREkBbddtGOqjKjz0B/u/c/9S7Pvtf6E3vyfkuSZL7MpFCDmbEHI2gFcIIQ8SQj5KCLkQwH0AXt5tLdxDZUXLcuRlD7KIljlu0OmraDPmHFSHsZXr+DH6RcQgcUPjPI6t3SRyXy7EQNHEQq3nrP7qiWRagZkHpIzMpRUdjjnRr8OpJztJktsSTsImfeGlda3o9yfmzZd/Crt+Wpz5dFAy6VBg/IHx+/Wa4U5ftCzbpxcBH388zGEdhsi5CdaeTYim2VkdVcFfdt9ri9UDTQZKSqh0XOS8FCGMgDACRhi+9+L30FsVZUHVH7M71x8xzoVYxEJ/FVfgVrWvAiUMsVOmP9ioZTAkNCmLMU6yaY1zgz5qSxudUuDpcqRiGAjh3IKHvq5CEAUTwlwXsCw4Rfq3jD5ypEjfmGkhCqWjuQjB0WAlF8OmP/M94d9xteLgK6SEBO+XEKAMefxpZ0jq9vP3/K6kJsnlcfYfvT+/LrMixp0e7Vi5oxtLNnckXrulO57u2fM8I5xdln/8bBEYo4FxPn08j6je8eA30JVzYhEQuuNsxhO+oUwIoBsHa59Sal7/r30vgPB5K8GfQeSC11ubjfccN0NVMKnmLLKyEsFUHe9zlLFIao0XAdwCzZQFFVNmeOv8Az1g2xKgbX3QZ6ikIO8OWPtA7H+rXkL1xMwvwlC2EiLnBi0HPzp3HwBh5Cu4niWgu/FR67hn2Wb9Pcg5d/zTc102/u/R2/F79yfwNr0adCgGXkZNlr3H1cKzJAVfhrJrbXB27lRKnsmw9lFf+Ly5gUMoFXvPV37L/mM9TcwkBAT9S0PHEa2pxYwnHseU2//OHeKEcry0dGGR5tD08Y+BiBz7sfsDp/7IeA9hTLoCjRCso16I2HLzqpG24ENYn1P1qXKiOrbXvLYrMLILLBpgyPe7WPL4ZiMyIKl297BKiQGX7qefBjwGp99HDmaskBBOj5zHPNK4qnGRbYx4qPKXzqpDpWpNBsSqiTdmSBFrRVJFgKhxHhIVe8H+exbzAGdf/h2WyjAASRq5p/v/lwPYAeAYAAsB7PK3vSsJ0tHfgda6sIaqgOR5BS+wJBkoSOtqVLoCpqMtei/5CtvaJ/m/1NIi52pnzc5Qa7bK0PS4qJOQirTRDF+q6geQV5UkEYXW/8MQcZJPGb1fh3YCly3tfTjvd8/jy3cuGdp2FpMkul0TbLhCgzI3TQcmHBB6kIeB7cq0UD1TEQ+5bddJocSkrX0ztQSL8JSnMxSFwyCNMmGPjS40AFcyCGhkwZJFf/a/Lr0p1iFACQ3K6RBQuKwA+IZJhgFfbK/A3j7/QZBzblMwBrRtj0eWrG8x70sdNTMRwskR5lIiAsMMa1/yBDeKdqzXDFzXBaEUjqSomMqPdbWa0y3kUo2ijNewiRNjrNrSMsgYCj0WWldURQ5jDD4CKDTO69CDWuk71ZYVz91b4U0Iby3zIfhpT+29BGubkxFN5bv4s+zsjE9jKWhrhczu7DAnGANx0rq1x4+cc0f0yQsmBftcMK1kVCh6JItU+HBmQnnJJdnqWPMYXMk434euVc79kM0N+wzpg0X6cf6IzxnvOfswzYGnGV4146X3JCLdzIOtzTEuKEAQ1CzfYrkAMaSTeQXgd0cBnZs5ceD+F+Gn7pnB7lIY1AcqHX3ciPn0cTPSnyTN43HfTxjncs55Gli7eGbXY8r7Eso/IxZynRZW3T0ahV0qUVrclLmxa2OQcy6M8/bVVRjV1Y4DN69A7ncXY/7TYZ5vRosgZm0KZwRHX5Ap2piS5qz8Bh6F7nr44XC3BGu3R5urBgyleLn4sZzW2S3Xo7eqq5AZOxYV832jn9BI5Jz5SLv6978/5Vqvt0NCWTCXG4LMU53FC7+CyhM+rZx1QPWdkSuL7qgzl8si2n7G9DOCbWva1sYcPcxSYsAlt2wZAKC/lX8jmrECvbCuXHUmnj71vcZrVGercf/77le2MeKFEXiJwJfYdmSbCdY+pJKJrpu66HpNVvQV/7t7LAxIDjuabjdI7MhljF2U8P/Fu7ORe6JYUMNngu207S03iMQwWMriEtufNvm5SNTSIudqZ60+4kjQqrCTy8Z5obcXhR07ESeEEExsjBponf2FIPosS/3oShxx7ozgbwA48ePzYq9fVOKenVA1olcIFUWrygaxfO+3Bt/q82EtS7YkR4mGXHTjUlZk3CLGz4f/IV3Hn8B3U53zddl4WNHS7o3Kb53BORCFSEY1WhWpjuaaijYlsbUDwNS77sSMm28w7pMj57Gi7VrXsTY2cp6RSi1RRgFSAAOBxYCzu/nCcGq2SjwAP86P9qxZFM01D5oQ07xB2Oawpan88mNLULoVWPvQ5+ZtW9MOAMhrZdCY6wK2jRs+ebi6TT9/dbvxupZEZjXQso2pJa7Ui/YhNj3diB2v1aHQrRtkhI/jYB4zpCWkiKY8483HL7e2473T3qsY5/uO3BdnTj8T/VuK5+3V+nmLwkAziU5ieNOym4K/PeYFDqsk8UBBGSeEkw20iV3bY0sb6dvv+flr6OnIAfmeIHKe6/Sfu78DjtTnF3kz+R9Lbgf6Q0eQBwrbaoZN+PPmJfbn2d94CDe/HM5tXRZLnG+F85EyLwKDFhB70SbxpSOOMnmMEQu5k3+C37unB5t2h1IpkBONVekh58rYjJnAAlh7knHu/3vUzBG45gMLlPNaenJol/JFA3Qtodjw2Ag4/Ra6HlfJO+NAQmfPPBsW40PPNdiF6+/IY/TmZvS32WAgsDXcg8wLUt6kfRPJAb/j6h9E7k1lQriUtfGqjz8eZbNnpzpWF7l8LqDmHadxdgNQ9MboTm6cy13ey3EnHykrS17rfWFaOzx/zSWE8XlRoEMVhIKFyvETlPNsksOYzLLgN6EkcLjL676u44hqKk3lYYqEE1OWeNhlgAEXmbxNpAn0uSpqrJzE63G6o8aTjfNCmN4TGOeFcM02wdqHikwTAFBdHImrz6WZAGURRs6T1rU9TdKwtU8ghPyDELLT//9OQsiEYuf9twtlBBV9EnHFVp7f5fQw4EdTAXC4I00yJCIXVXPOiZbbRDK24u2S19D2yy7B6mOOAQC8usFcpiNjqMOzz1X/wvt+85zx+PGzeNS3cRyf2EdNrsH7Pr8fTrt8nxQPo0p0oAczkfogG57FmAPaAQCVk6pD/fiIzyhnZ2JKxA276Er2LqkES7HI5MiQ4ChQEndTznkambqNYd+1XhA5Z/3qwiAzflbsx6G2tSefpF6kYQow9ejItVv7eZ9s6UsowwKgfO5clI2MYzsnoKBGwjsheuQ85+YjhHBCKAmrhBJGMabeBkAxL29hkusTv3UUMHdMDfpy/NtaKaHpQmaPCSsujK1LRxhmWhRF5HzBxHqMqy+BeGyY65wLb3ukyX7kfEyDpBQayu+NnGyOKMvvWbA6D5uUpcitIwS5dv4uXY2Qhp12jRI539wW5fNIo0QXYOHQ/n5cfdTVijGftbL47pHfBXMaEs7m4oEb6Eu3xUD1EVWC+h0emduxqQNT1u4fAXiZhBEK2+sH8t1KJLE216PASXf88EdYNp+vFzrMdOeGLjx58wpgw7MgADo3VGDtg6PQtaUMqBmnwNr7kcUMshm46+PA3SExJYOFfokULoewLf0FDz9/dFXw2yGIkn0ZooWVTg6WzhrtG/1iqyBWzvt8MvdkT/V3SH2cWtE8ymHuyrKURD4ntzPGOBeXkwnhaEzO+YlzR+PMBeOVdqxvVseFuM7KGeeiwxJpbunaPKZqTBA5ByFwFDZqyYhzCUaTNthwg3xogEfOif8RR9z9PFjeXMZRrgUdiGRgkhRwXcDnUynmvE8pl82/NPg7FaydkOKGFmXwPIL8Zg4bZv2hcR64XGI8JVZTU8Q4FyzsBD6SNOBVUo3LCJpGmnwoJRg7vQ6tW3r8a8ocS+r4FI4/JbDwdiGfS9Tpmj7uV/nwXyHJhIRwfa46Zjr744NR+jf2EBM5zxgi5ywKYx9SUGccY/ve5wZ/6igkETkXSMnHl4clcYsRWu8Jkub13gDgXgDj/P/v87e9Kwmy5sNH4qoLpNfrLw4sL9XmBgW2vIQGm0M/q6hmNAtSMCF+zrk9bixGfu5zqDtdg7BkMoEC4XkMmyQl0H0jhHefc93zxjbHQeqWxShyQVqxr1QRQjBuZgOmzB9hPD5RtInDbmoKbyLnSDOgfkYvpp26A1XTqoOFlhFVCRARiE2tw5yPqoseeXnwC+Hfes65LgqMgqr/DqGYjPNJCYRwAHBOVzd++GcXX7vNCyDozi4tOiw9e9m0qZizfBmqj9YMcc+NKsAAbl9xOwDgluW3RPYlyW37Xo3lI18AwCPnYyvHBvC2fZf8OnK8Pmcv2bXEHDlnHCRv+bm9lBGMqqUAI6jKauVdNvRiezs3YKit9uORNdFF56iZ4fhYvj3MofvYkVPjHlMRIyGc30/G1JaYcSR/ixJg7X1deezcEG/gBRKj9DHPAywrmK8Ac+T80DOmGc+3pcj5oOusFhM3PgfbJE5zC+wxITqETT7Sj5zzfvn0qmaFNAtAqvJqLmgQ4ZMVrbTRMQCY5FBc0lmOt17cHt9+Tcle2caJqu64+hUcuvFM5J100QmRS03sUOEmzEVu42b0vcXrj7fecAPg94FYgqbaCQABPIc/Z77TBk78jkIIt9obj6yAJretD7Z7zFII12oIXw86bL6+5KTX/vgIBlAbhR4p1cL/LmO/9z20/PkvAICrn/0dLrfvUZoo2iKcRtUV/JlzfnQu6xsFr/zhU+FJJDTOb67O4fc1/UXL3A2FiJJndimeANnYiTHCQpRRUs45F9NjtutRLz+a19Y4B4v2+wzf1KzqSHGOWMYYJ4TzH9GW5hZlmQWQRQEZuErJtjKbBmkU20cfjFx7F1b1HY5H2q9Q7lPr62C0LnTgyU5qElOZIyK2pVYjGISMKAud10lzw0FjDkp9TTL1KDCHYs0JJ8Bpa8OO73+fXz8hHU7I9AcfQOWxxynbthc4SoCInHPRv6T1qL+ngOfuWqO2AywgibOyFMxjWOdXXJDnUB3dJ4xz2Wh3+vYMQrjKQzj/kFCDSdZGf5lY8wjGtDLM28D7Tgbx19b7ggxrh7QOCz4B5shr8zDD2uMcFu/9ORhjeG7rc2hjryu79Mh5c3foQPtPKKuWZnYeyRi7gTHm+P//GYChjsK7IguxbBSkSDQTHlQZGgQKCg8HVd+GsyZcg7HZ5epF9AgjtQHHhVVVhRGf/EQAPwnuaduA54G5Lv7w9Fp86A/R0hx65FA2FErKVUW4GLs+7H0w8FK3vT342x41Kpz0KVWhgMzlhEq1LtDbGi602nO9bWNTXwzlfFW3AEw8FLj0GfO5SnkS/8GGAeboGUJfx/WoToy/blWV929KSlFQWknvfwbkRUTcgpH8I4C1l+hVbqvcjtZKjlAJcs4Jw4mTT0Rj67LI8XL/95iHjkKzMXK+zzaOMhF9nDALea8A05Q5q2BhlKsq50IuOGRy5PgbPhoqReWSkTkoWPtAu4mVBQ76BFBeH0u8aJK7f/4abr86hsVcEYGAURvYt2gRel94AeWzwmoFzBA5jyOdkd9zRU3ppYtKEtccNYuT5reqkZ0sfXfXVSLnACLGeTEDmzEGBzYyhOdxy+XNSjHOx/oGbqYj3sCW2bABoDvvs7N7UopHCqHMBeomIr9+fbCNMA9Ocws2fvgjATQW4I4ZE9ydUgIc8FEOf/Wl0HQYMO9sTBoRok4s4iIvIuRuHou8GVjqTYYHy0DVBtww6Wp+X6l79ZURzgbeITusAFJmo/6cs+F18KjU5K4d+LCt1qEWJZ/E9bI+miPnR85PKfwLAHBg893Sw1kBjHur7aHDYmjtKa2vDUSOnMHX/FKWFtlx5nYmO+Wo3Lf1NcK/p7w+/9svN/md+5eqx0qBDM+vvdX6578qx8SpLC5zQVmIYIgTBmDm6BrYcCSSMoAVvODkQrYGzz+wBf/q+CJW9i9Uzqc+L8KUm29C5cEH+xujQZliQiwbzBA5rzr8cMPRRURyniRFxKf7nEizGmYVLa+ICfuFl+8OqzWkqelu1dVh9Ne/YdxHCHgayh/9KjeScf7In5aifYcaGSZw4frjPJO1sG1NBzp38X7Sb4coBtdzFUNcwNodyQHdufVtgkDHzdc5cxUXoee3reLEvNS2sGIWT2Ea3zkDv/ydi2/ewr/5mIp4eLi+TnjEg+v31ZbrpZK+YswqKFwOa9/nuBA0PaSwdpPD4pBLgfJa3L/2flzyyCVYZ/9S2R2kdhHh9A6DRf8Btnkq47yFEHIBIcTy/78AQDL29F3hUCF55fcNEoVUw89Lp8TD+ApDGQ0dZkp5vcM4z5uIUDDHwYvrzNB1GKJTQtbsKq08mlCcc718EGfK00G4TNJ+u1RfVl7c9JzzN24P/1737+B9tlx/vXI9AWuRjZ/dIrpxKUfLPQeYcCAwRmVXDcT4XYfBODdEzkm2Svm9IJfHezKh40ZuxbF/PxZ/evNPyE7Qslti+mVvoZdD9u79P6B7ewS6BpRGCKdLmGtGAAZUZ6vAwFC5YF+U9zVrB4d/duW7/LOi9xzbOcPfR4J/+5w+gNFIXuh0x8I+fr0ey6LY65AxqB1R7j9XdJWwJSfGD84OU0DKU5Iy7lgXVY4H3MsJAU77Ca80UAKsvdUvpfjIn95KPtBsmysy5qpv8j8S5iZd6ACcGgMWtzQljnlEeRZe0z2cxz5zwsxIznLWSnYwMAY4wij2XGRpeHyaqLuQClHKKOGUnz2irkUHjVUjbGVuJdIIJS7QsUmJnhLmR2sIwUqJIdjr7TWyJxNKgNa1ygRUeeYlAKX4/YWHBtsseEGUG8zDhIZKdKECLsvAMpQLylnVkW2UcG4ALycZaYzw0o1d20HK4tc3VzPOxRgXxrlRCMU/XlPLp/buRpbhUoaNnH+66X8vSzxWIYTTBucxs7jxMG1kuN7EBYxZn2yYxUfITeIxDxYjRuNcaRIjmL3XPFz3oX2CPGgAKPQ5yiDZvMrskBAGNclmAf+bKyVFUxvnFF53D1ZpEeY48j1Zat+rIihlXqIkZ/dRE47CHaffgXNmnhP/EYL2SX2fENScdJL/JwkMdFpbYzrVb1Mc0YoFvPUPoHkF/y3pEBvfipoZhHg46dDlmL9wApomqGNYgbUzF49seCT4vblzS7BdSM2ocF665xevYUUCmmhIJS5y/vDXjJsjQbisBdfiwatIoKWEABtHGPK+umn8Qmxd1ebfz7cjpDHveQyUEEzfLwwgDSmo04CmxKF8ntnWsy26D8Dqdl5RyvOdry+sDW2e/xZY+8UAzgOwHcA2AOcCuGg4G/WfIDxfVTbOfYPEn/A9RuGwClDBMGkq16PDTKnNlb6YCKVM5CBD1CsLIZtnz4tqNP3pVZrxIklzQkkdIFzkCn6Jo8xQ5X7K0TK9zrlsnDOXK8LQjHuERtFuL2WpT7yuA/x0DvDMz9U6nhfcBXz8sYQLDV/DTcb5DC/8dqN8j6nPtYdj6vaKHH/9kuuR0YzzwqaNkePeankLh9xyCC6+7zwc3uw/rxU1zgcaOQfCnKPvH/59gBHYlo1epxdVRxyB/go1xcI0Z5sigZWFGrTn2oNrU0bxZstiAAT7T46HUVOL+P4kAaPzQAlwydFmeLYwyMtsitGlQtIlsf3nGvCiRO0B5ZyvfGlH4v5UrfEVWLfLHDkwiQyxG9KSLiYpMXJeN7VXUaqZ4yiR83MPmIBGrSRQmZUcgfL8yDn/4WBK3ZRgXzGH1tR9wzGQ9dekqhhDkzGGx5erxKE3vBnNYnNSfNkgYp2XykwxF4xQeD09YH1hZLT7iSeMxjm1CPD6LWqlOp+8slyK2lnwQjQC8zCqykae2XBYGSwaXcd2GSCtFuWRc8WB7gJgDnDXJ1A1gj/HpuoocFAY5+X+e62v5op/zkeVfaFwSeQcUKt4xHIYZOZobtSUwk0hR3W9YpHzBE6T42ePwuOfPwZHzQzfYdzwZV1h/qxnlWHDxBNgNTZi/Q9OC7cXi5z7/aazUjIcJRQGoQwor8XISkuJnFsWRaYtNP6645AmvhNOgbLLUP6UxjksG25LC5xtmiGSwjiXOR14m6S87IS5gRKKvRr3AiFESS8yt092hhKFnb7uvadh5GeuwMhPfcp0JoD4smXM87jDXogTzzwPAORzy9BwwY9x9AdmRZCaMlu74zlKlYkvPPGFYHt47/Dczcvb8OgNKnJj2CROx2lZY96u9SFi26jp3sRPqVKde0n9xdHsCa7b8Guvmvl+/OOnrwXXB4Cel0JbgbkMxNK5CYYycm5Yj8r4mC22vj2/aUlkW6ko4HeiJD41IcQC8H3G2BmMsZGMsVGMsbMYY1Et/F1RRC7DBABMeKMYsCG3H67bwUtCbMwt4AfkQ6hQMEh1pZBwtnYSFzkXuSKFvELuNrd1XfB3bvnyyHlxkuj1R6gUt23nHm69lrGQ+9fej+e3mvPcjddVNDEt5zxysHlzwOCpKT9Oc7wzYkhED+Plu4CurcCjV6l1PGccz6PopV5vCES8k8/s/5lg2xEOwR1btuHa7btw69YdQMOUQNndt2CIaBESyRHuuP+ByHFv7HoDAPByxyp0WT7g8ZWosj8UkfOjJxzNWUUJQc7JGfP9ZCVBvAdTzrlLC9jcvTkwQcQxY+ptvGeOuTwOpQSEElBKgkhBS3ceHgO+cuoc5dh/ffZoPPLZMG1FVlYHIjs7uAHyr6XJxnKsFBtnA5UUkXMRlVl7yqko7EjXfoHWAXYDw3VcKbUYsQ67SEUBuK7C1j6hoRJfO2Wmck5x4zxkAte5K4rBC0/9333w8Z8dpWxrrDbf7/4l5ijF/3v2/ym/bWniLVCz80IY51RKQeCR8+i4LGzdGoyZmU6YWxg4YWSDSuSwSwqdBam8GfMA5oJmsnBZBjaihsdrW0KU2PU1/dgyp4o7tDUlkXmErzH5nqAPl2UMaTn++6ipzCj/ikj4HW6UBBN2+e53HgO4+IipuPN/D8PRs0qYc0pAtUxpiHcyUkowbaQa8bRj9AbWpToB1k05DZUH8jXzu2ftndiGl7e9DHgsiJzX9koOIn24eC7gqrB2Xa8wrRuABPe37XCuy0gcC2kJ4WKMeJbGYapFveVzklA18lpbzDhPeg6SyWDEpZcm5p/HOaGY3r7tbyS3Q/5EcSTC4Ibo75b8Lvjd66dIyJFzd3eTBQuJ03FinMD6c5KMhVE7XwUAbK5bEWzfZ8Q+RuM8O52nL7iR9d0NWN+V6/t6U98rr+Lea17Dry99HE7B4/qN5BAZ0mXXZJwbgjgm2Un/Fdn26oa2wbbobZdETZgx5gKYTAgZ5qS+/zwh/n9CZIXk/rZQ0en1EsiM9MEq2NrjcnuFMeJ5sKVBZMlRnHweo2uL5wntO6EutuRN0JyUEauvPP0VfPKRT6Y61r+w9Hd8nXMg3ncn5l39ETZ85ML07RiI6Ipnu+bHKgJfDaTD94i+cUfycQMQMUkfNSFU2C1CsFe+gIV9fRjl58haftvp5ih3ASU08nJpZRTuqhP2bLMtI8HWfqN4TttZM84q7WEAjKvmdc8ZC3OjHM8x5rY/ff6X0PnPfyptMxnnK0a+LB6AH+NPlTt7d8RGegQZHKEkGDt/e3mT8dhZo2swc3TNkC1w3b2DzFVVSn2VJs/esSp2X/0Y3icOP5unCXg9PRFFTXaiiKoWxWTVy6ERPwyciaqYYO0HXqweInE2eCP29dOPBBNuNOdcb3IxA5tHzv331KMSMaZxaNlaykScsrxkc7tx+z9W/yP22jk/11M30okPJ28478xwG/MizM18Bw0MoDIilfWxCLDPB7SD/bZLTmoLUnkz5gGehwlNtXBhYyKNwlU78n6ke8JfkJ/1Izyd6+HrmW6cM3+N2fJqEFFvyERh8gvoGr9JYRoMACm9zPB9M5WYOqJ4fd+hFkoJDpjcWPxASVjKslNrvn8qDk5AFpnksoXceHjvPmrNedbRrvz2rGwAfSpWD/7NljdBWUz6hrSNMXArwyvAk8rsxUV6AQ2iLSLnlGLUF76AspkzAwcCgNSEcLlVMXNoCgPS69WqP6Q0OhXj3MD3IYv8HGmg9rrERs71mXDe+4I/m8Ynjw19ypRLqZ1wxwnY0r0FL0y6FwCwYN2JYB5D9etTUV7gziGvSPBp2CQO1h5DPKpzERDbBvMNV08iu/zzKX9WyhhGbqu967ICMzpK5bVh07LQyLUyVEWsDXfOOc2gtb8V1yy6JvFU5kT1zl1dpTnU34mSRq1ZC+BZQsg3CCGfE/8Pd8P2dOGRc2nyEwanNnZIUl0aXSmkyZHz3DIeFS9s3qyUSZHJh6yGBhw5o7jH3GUM37y3SD5pChlQ2S4F1k6ToV0xpei8ANbOlMkmv3Zt6e0pRfRvM2qu+tvkDfzsW8Ann1S37fTffUu84TNQEd+kKhMufpauxVAryB20DK847+YjxtyoL34hcpxuBBRi3CnjqriBfdi4w4z7k+TQSh6Vyvc5YB5XPh3PMUYjCpkqtN7wZwDhe6CMYmPdMqxrCvu7DJETxwT7YtY/J+9fjxKjP+mq0+fiQ4dMUrYJI36wyOyGdclwwKJC7ZLY2mVZ/OgmbFvTYdwniNsmzm1EYds2rDjgQLT99SbtoGQF9ugPzErcH4fYGTIxRTTG7a/83HHDQ8Hf26+6is9Z/rzV89xzCls7AKzZHsMJEiMeYzia+vC9X+6n7KOgwThr6O8MmNC1g9TrxdznD0+HKKuDGk+POUqV6jw3xlb4VROEWIT3JypFmgnz4BmM88y4sVh3wUf4eV74viklQKZcVcTFe5QMaRtOWN6MAfAcTBpRw3POSbRfHzSdo18yNctAsy3Y1NqH1Tu7gUKvaryVjwjXGH+OrDb4V0eA939hnHf7kfk3tpjHBX/QDMbUDTyVZbdKyjJfFiWA58FqasKMfz+V6px6H2UgEDBeJV+XnM1Rx2b5PL6epgkMUI/BM0wNLcukyL1H+ITuFhRD8ambo2SiwSmScd6/1IdC2zYq9p6HaffdC0tibk/LzJ1ft864PU3kvOtfauSQeS7a/3E3Oh95BP1u/LqgRNWLfV/5OUpAUQiJC/QwXe/wjcVbv/0iWiR0i1hHZMeI3gcWjFoQuf70Fj5XTt45H9vWtKP+zRlYuOaDiW0adomDtcd8A93BQ2wbzP8eP/1DOFdmaAZTqicHvzeN8KPm/veaWDsRVx50ZbDfoa7ZURrzXihVYe3DnnNuZfDDl34Ye8oR448AAEywTxjChrxzJM3rXQPgfv/YGun/dyVB9Jzzhgt5mr7nahAVk5oklGRdKdyyKDFyLiKCvS+/jIwUfd5e2RT8veuX16bKc3tzS2ck9zDSzBST2y8X/bLoMQBQcUBYNi4Kax+ApzaAtWNAi8mApdiMZTLO6yYA4/aLbh8mEd8/IxGzeR0b1IOaVwaTg+mJep3eSOmXoPydfC/NG5WP8baWCms/67P74ZwrD8BDZz+EXS/yczcta+WRc0LgMCcmaiEZ2SJyDgqPunh1dsg+LhxrSdH1OJEj57J89Iip+P77VDLAw2eMwMxR1fjcickGaDFJYt9OJVpkt1Tp6zRH7pnHQ4+EEBQ285KRnVK+Ir+1tDAbppT5CydgwuzSonFDKmIe/sJqIOsvfXPPVA7pfEV1+snRpbZbbonMY+t3lAa7+9dbOzCFmAmLCCGBPvWXR3+A9eecGzlGh/7HlaCS5TMHlYB2QjRKWbD8iMaNoZFPYIa19zzzDJxW/k6s1pCQjlDCnYCywWwwzpsqbXzgwHHiAPCqHhzsTg2w9n+tbDc/xNon1WeSprgxB/BzKudMjJzWjyymjaxC86buyL5YoXaQGzl3bG36894GKRZZVcR1YVVXIzMqnjlaFlHS7d7XOWrGq+dR/R6DcS9g5FaKqB1lwKym2ZhBPqZsz3WE6x7zHTnwVFj7hrfix6cSBfYdizJqTIG1py2lFlPWLrcy2Tlv5OlwPWz7ylew5dP/hxFfTKd/FSvjJju6BxI537TM7IyMwNr9lB1BOCpk5kG8L2XKpNx+ve62oU+M7AnHqhjLWYc7xHpyvZHjd4vElTCUcuRlKZ+jcv7IkfMxbRLxKGMol0DOu6Y3onzuXCX98IK5F6CpvAlHvOVhdBtHMUW+gf+iymbPVu9LoeT5D6Y6U0RMxnkRTo7jJh4Hm9o4bHpUNxjSqP7bJEU1TsbYtxhj3wLwcwA/k36/KwlCoLK1l+/HIy3dWyq040owznt2JUbOBetxxYIFSqRBJmhxW1t3K5PhU5vTec/l+UEuvVMsF7bQY86FktcPr3+QUcVSRFc8d2okIxok9e0QYQjL5Gu1BmNS7J3wShadGw0RHg3ZEVfTVpY8IcAxX45sL5UQbvxeDRgztQ4TalRSOuazisbB2hmhyK3hEFQx8VNmgcFDs7cofBZGML56fPA7bfkogENxY9lpNamryOCRzx2D2WMGqZwPdkxrkd1SJc5RJ1he+T0EzFvrN7ICGxMpGlJFoFQR87CdBaYd4/+tpgaxnAaj03POtXks4xuM83aej58v/Lnxtr96fBV++xTvq5+5bTHyMOfgUUKD2uQZJya9QXt9D7wRNfSvfUw1BLImhSlR1D6ws4o7okjvTsx48gneVtsyRmvc9o5gu+WFijmlBGCeUkot6OvSXLHv+Gp86EAxXhnvR9SGy2xj5NxNUn3kW4mccwCZKg9WTZXRWXybuxA1Eslerp33h6zvSN97vGF8W5lgLf6fQydF97+DRIa1ZyZGnRPKscxLT4QGKCl4AOA2cQJDalr3/XGlnyOksGMHWm+5hZ/vAfUVjRhrHWU8ljcWfB1rWROUwysmsnFu+21VKhJIxnl6QriYnPM+s8EmZOVBB0fPkebQ8iWrU91eQKfleu1q+8J303pDlDOmmLRsCZ1W7OuyDqS9883m8pxHnT8LF/3oSJRVSo6PFEtCd7Y9PN7vM+O6eIrVz142z7u7S37cWI9TJkipHDmzY08vuwnL8iPnqlm99vTTseO73wUA/PU4itynLwChNBKcKrPKcMW9HqZv55Fz3VkaGPOaPkC0yPmQItbWPmHcnFTFhBIKm9ig1FB9aMga9vZJ0bdLCDmQEPIGgCUA3iCEvE4IOaDYef/twiOAUs55zKumxKAQL72H/6vDXKgFOG6sN9YeweHqzHHVQaQNbr2O7XBKuZUSthfnuS2Scx5nt8sOiK1f/FK6NgyFFIOx7UiZKiBqnmejJX8GKyJqZkvKt2kyW+UrGWMXlWHLc1KOov9u3Wf/qBxvMs51z2c/IUDDlMhxgyGE2/c4riwSSpDvd31jJS5yHtZpDaPiRMndAoC5TfNw1xl3BYyvcuS8eUtydIwSc+R8WMUfIh89fMrAzqeDi5xbMYXWe9pz4bsQxrlOPiSViul6wrxIb1+bzBA9rCKMcysLnHM98OlFEeO8bK5K+CcryJUHHxzJ6e/zle721iacMNkMy/vJv1biBw+FBJ5BzrkmlNCifhVCSFFl9qdaCTU7gQTqifJoNFqPxme8MDKVGTMG9pgxsCrKjeii7qeeCoxzKpU+o4bIuQnW/sbGlrD/ei6PhBILHjLK9UKJfxnye2KedqRFwba+rp+CZWyyeqIf1ZszliMtKg0kcqCZwM+QGe7UjEGKMN6skSNQNqsIysf1SsrT0aPgvRfzEkrEsO4LJ4Fw+I3V0gI2/+9l2PHt7+BQayYoA2BZoITg2bE6gZxA1hEeDHny+2AsnSEtO16Z6wCWpUbqlMh5SkK4uGjqQGQARGd2A48+1mll2YR0PxkGWdpvvwO0pgYNH/lw6uvPOnhM8Dcj4TuJi9rqYtkUlbWqfpGmSkdrRUhwqTt4TQ733Vk94ca6WmyWHTn5qF7x+PIdyGuoFWLZAKURoz2/OmR7f2xfApSXAZaFwtataP5tSI6X8aPuhHkAoeit1NJc/f6tE/5ywlvp91A6zGvGGDcnIbwsYsGiFphBb3mbEhaGVNLMCH8CcBljbApjbAqAywGU7jr7LxO9znm8rm7YIcqFGQjhWKGgTP7KPX1jpPUvf1HWRr20SaUEDTpsWhSKnFbSeC7TwCeBhNwqvc65JuVNZjivXF+655lngr+rjz02VXsGLJt88rQBlARTpNaPAh3/zcFdxyCu/z6LQX+WlJuJA/fewN/tjntVr7zRONe+f44So3I+mFJqE+ZwxeKpWzhzabajFgWvoEQj5iy7EQBQ17EG5XPnKvekjAZkMh/85iEAgPfPfD8qM5VB+RFBCPeRuR/B0qeTSctE5Lx9Ry+qdpcfzDeOa8pLjXb6QqwB55wDQHWj2Qm34oUwQhv0N984t8eM8W8dtllWAmXJ9/nGgZ97mCmzcNjZ03HoWeYSdUMqjmScZyqApumRQ5RUHMtCfvUa0CqeO1u9cGEkbeCQydxoK8QY3CahMZniBESZ7+JEVmbTLPw6FH7r6NBR0GIZ2kKAaTPDdszsekjdncmAuAVzniNC0lTZYc0Yw6//dT62NhwSbguM8/A6/fl82H8LvQDzwPzSc9R3vPWzZPbfMxeMAyYeom4sFBTrnFgUrD9Uon9cOA8fzH8NHqhiZohXl/ejrMZ1UIK1Z9/hxnn3Y48DAGi2rGiaWBK6zyR67jCt4SgDYlK6hZPAP2evMWqGpSCUHIc6VNByEEphUYLljZPVC/m35LB2fp+4AIouiuPViXKbKHm5aSPnCca5MBi9/v506QUx+tK5s6LpLkLs0XwuHvV5M52U16PCzJnrqnNeEVEcGjIyRX/nMdc0cQykMc6314bpRpF+ZnDGpEW8DYsUVJj9T/+1Ahf/+RV88x6NwZ5aALGiEXVJGOG6FPHnlV2/+EW4L6hS46GQqcYr+38x2Nfz3HPBGNOd6ISQ4YO173O+cXOUYV5tT0+hB2s7o+iQ/4rIOQCXMfa0+MEYewYwuqLfFUn0nPOIh1AcVwqsndpgjhOWktFEeGm7n3hCUaxktnYPBHct2oKmqiyWfvsk3PixKCwqrVTUFIcxpyaEi/P26nXOU0peYuIkUj1cYZgNm6zhSkx8FLLEaaMEJSetOIz3rwzN4CDXwg92xpeXM7F/TjdXWwLJRPtDR04lRHIAI8HWYCLn+qJLCIPHPMXoq+hvFjtBtJIvhFF4xIPnVKO8yi9H6C/SZZT3Heo72oqVvBLt8TyGm7/5Ai7rTF9LeDDiTuOG4IBzrUpka595kFpOLpVSE0TOef9rvPBCACqsvfGC/0m8RGUd72OT927C/idOxgEnT0nb5IGLmxcJd/HHSEp4ZgJ3rAmFlrlO5P2OrebH52Kg6iaxY4xzSmikrqtrqEUt940j+ovfN8IonyK1Qx6Legkzkslw4q2YMe75xrklGedLnuA8BYsnfjw8UHrUKf23oJ9lYMMN3y/jsHZBmiRSx07NX53Y9jKbAjNPVIwHp7NHIR0lloWgPheAh70D8bw3DwDw43P3kR6WH+MkRTEtO0B4xcG03ynS8+yzALgDVo+oRcRjJcHadSE+ysBknMM3ToV+o781t70dAHDWz17hkXNKE/PTC6QS8ApgDNj6Sjpei65/P4NcbwEv3bcWXsFNjI4bc8JNkmRo+kbSigX7YfOnPl30UnFz8ftmvC/+JOGwjwv8aI531tub6FBIapNSii5CCGe+pskQ1z+rqdLLovGcLG/12JcjDjKTcf62kcQZ5NrHudH53CqN+4mEsPY48YivSyWMw64ankrjSTrNxos/FjjfChvVSkM6rH1o2dqlMXTJ08CZvwYAVNnxjP1CV3x150tD1453kKQZXU8RQn5HCFlICDmGEPIbAE8SQvYnhOxf9Oz/UqGgQbQNALwYxcbo/RLGtM7Wbpf7xnnMYiBNlnGR84cnH+wfSlCZtY1wutG1ZcikUBayKaJ0g46c71oGLLsv6QZG6S9IeVfz5oWHp2SdHbAUM3CGwdguVQp+v8rQDP504h9xWk8vUBfNI7yitR2W4XEMKT58eza6sMu1RgHAJQRYfHPkOOHEsUvOc4VSe5M3hMBlavqH5XDeASax/4t7Eq8CjHhg+cZANxD6gyUUfH8hT7MgEUqUfjlt5PCXS7KXdcFmwF6jB8jVSe2SYO06/C8VGpCInHMfjSAUhxLIhjJlFi74zmE44aPD7GSTxc0BRZwycr9geXXeZo4TcTIS30FVYCnmUP/l/tDRS4qF99aVSqe5JXpcwmqvG/cA0FBRr/zuL4TcHVvs6Heq6yGKEk20iSK/di1Yd5eREA5AYLSTmAoc4YHqdR1YvIxaYJx7PqzdN/L8wbidJZcPo4RE5ud8Z0Y1ACkBGxn2PTnVYKY09mYezcnp8knGOc0EiDp7KGHNwyikrHjkHK47KMXdtoVxngRrh/+v+T7lvY4Pa+fln+TgSNWYsB974Iih7q1l6NmVLgVv6ze+iWduX4WXH1iPLd21SlpOpL25dCUuk5AGsjOk+8kni18s5vskra0BIVyMMWeC3ZMS0B7y/CTn7OeYtjaWoB/pBvv7Zr4Pb1z4Bi6Yc4F0ENCT6QAYgePrfj0ZHjAwGeeffzxacWa45capZ+HNBL4eT18TCQUINcK5g3N849zUr8TY7K0wV22S+SUmTguJDqlGCJcGuZBa5L45Zj6wH/+GIypHxJ5C/iPi4/GSZnTtC2AWgG8CuArAHAD7AfgpgJ8MW8v2cNFh7XHqxuzKx4CRar4imIsNLT3RCGPVCLBCIda7KRvn8uJoSYucFZCBxXfsqSOqjMraQGTQkXMASR7COKNAjpzXnnySdJ9hZm4vjyFUEWLIK9rdUvAKICAcQl4uiIq0/lA/GRXMgwm9KrY1ztaiAlq/vPGtGyPnegBQHyU/EvDxoYicZ1qruUEjKRq2K4xzKyAILPjMsIQReH50TYwbPfogxnIa2L2ux5wwZ7T5wAFIUoT66onjcZpWKzi1kNIi58zVjfP080UAl/OVO8XZWKzeLiGoG1kBK7MbjRknz8ngYsTr7YXXG8I+Izn1jss7hfR+K7p5VKKANMY5MKGhAs3MPLdYxIqSfBqm9ySDqWCYfzOaoi4rs7+58ED9cBy+DMhv2Bwej2h/kuucbxt9MFrrQybiIOfcK1KjVlNWXVAeOQ+UVQYwN4C1i8h5XM5+0DZCODotEs2T/rQomMSIHOdcGb8PTxcr+OsQY+BKpySMhpHzbAxnwztNSFnxyDnzSiOEi9wjIXIeQG7FsQl9mnrc6M07nqJBEIuFsHbfOHdz1AhrP+iVKNpi25hDke/3ofCelwhdL9srZRWOBEO3858Px+4zSZyDUzfOFeNGRM5jnESk0oAAKwXWzszG+aMdn1UPHH9AZC35xC+ONrcpxjjU0W0ecQGGwDgXQbMZzftFzl2xYmNk23BITmr6j7EIHxxvzrkGAEcfb73NcAlJNs5pfOR8U/t6AACNSWOTA1iedG9CydAa5LLIfVMh/Yg/JUkXK8+8/UGwwUoatvZjE/4/bnc0ck+UtIRw+1Xew/MYZenahst/8id092pMnQdcBOYUYiPn8iIh1zmXI+cC4m4ZBtlr33gPfnTuPjh4apOSI1+VLd7Rq+rMymta4zw2oj39eGB8PP9gWa35PFlZFTBaQPUKDosc+1X+75QYhtgx+5i369Li59HkUsLiSpCCV0CGZrhiEyyw2izYvgE2A2xT5FzkwOk6rKYo/fiVH0fOdQGgJmpABvnfAzDO9QWD9md55FyCtYuFiBEKVuBOr3+s+od/TweMeHC65wTXCvKyPEFS5RuSroVJ85J5GmxtvJSvH7qSLaJfH3LG1Mi+QvMgqhJoxmMxWfOaVnUgjW0ucjuDyLmvhMuR8yLj821hbXfzPN88RlYfexxyq8K8N7dZTRMJI+fhs01feT0AIJ/COPcYw9QRVfBiIgUWsaI554bvoY8TGXLd1huN8OlRSRnWfsLc0dj/pEk4ceR18hHIrw2VWz1yzq/BGYJ7Kkdj2ZwLsXjB/4VNJgKd4uK4WrUElO2Ga6HuoHJgodySI+cC1u73Lx8mr+f3j29QlfhbX9oIUBuFbvW4Qq9WvklSWIVz5c1vnSReAYBwfRU55x5jwMceVa7LaMjWvqdEzmkmmy5yPojnsXz9hhrmo/a/3QYgnJ+Tgrcicn7v61vBZGSLF5qlDJQT7044WEm3qOnagEyhGzXdm6HLhkknhX3QTYa1C6K1opIwfxa2bUVhe7S6gil1RbTJ2BZSJHKe4GQonz0nurEkWLvUPCdGJzzrt8DM98Bz1JcRh9CMQ02U2bpx7mFE5yQseZTzEQhH+8SO6DNV5ndPScN/VJuJfl3PxbZuNW+QeR4mHMWRUNnaArDmcfR5eWPKYXCOyDk3fCMRXDGmjQDcmeyL7OfRYe3WUK7FMXqfSMGUZUIlZ9s36Yqja8vwxZP2wnkHTojs29Nkz1gR9kChhKqR8xi4DiEMyFRGtt9f9nUU8pqybdlAwQk8y5FrSZOrPGzUyLlvBBm+fENVFucdODGAjB01k0NKJjZG26cLtQfZlWLZ2u0oUVXNWODD3LAqbwj3ud1h9Eq+nOzpH3ZYu1Di4+BZx34t3XUEOcgwlF4reIWAsRM+3NvUBylYYuTcqRkf3VlE3CJ1zgdCCGcii2GMgdgWRu/g+UgCIukRK4Ad5xweoaPMQr5jP+RbjgmctkG1Jn8kEcbHs/eHmdj4Fl8oa0ea88l1AzKzeujQEkIpNObgDcarXSKsXZc0OecCOikiy0HageREKUZ49LaUL3XzibB2t6Mjdh/gP1NMTn+ayLnHAMdlsbwlhJAogsjgFNXfnRND0iREd+BSTV047H0zMNP6F16a+AAAoKNuGoj0jJREn5cwBkYsdFdF544gck5czKlUWfspk9AITI+cW5hUn5XWCd8412DtuoP8wMlRRbz7X/ehZZmWGiJH0i2q1IQuwMLo2jJUizJqYt7w/xWIBNdjQKYcXSRUyl1ihbD2d3jOeSAZu3jknHklGW66WJZIJUqIDGrzs0mIxwJY794t67Sdoq007DeSsu8RGyQuskitcLx4biKsPa3Q8gRIveNiwwVRZvS4eSeeXDehAV6yQ6XxoxdGN5aw3sjrQ6xxvuCD/NiUqZBxa4FeIaguNwL1faOw8fU2AECFE18Bp6N8Z+y+oRQ3pu2XPnopTrzzRGzsDJ2czHNRMz6H0Qe0Y9IxvF48dzbFB76CwImhYo1IVezXYO3lfdyprOrL4T0oVQnh9CDEoCQm5cKUilFT7rPNGzrAi189AZcfOwP2O5xgM43s+U/wDhUCouScJyqveuTcl4bedcD89yvbmOPEwtqz00LmYhoXOQ9YsRPgYP4+RbEoIgecPNm4PXXkXCZL+oEEJaN2qNSKnMd9PwBURPMHCxs3SPeVFE+/rjLPl9td9Nkx7zdb3NEBADjoE/zfyUcMTXMk+evSv6Kn4DsyfAMVGWlBu5K/x3GOG2ucz8jn4WZLz292AeNEPJjIuW4M90zahrZcG7bnmjF32V9w7JOfCoxzRiisWq6UT6/nkEPiZeCxDL5+2rwIrL2qk0fJCaMKrLeyNovzv3aQct+/Vwk4rtoeNjZlOcEUIiCBZoKcQSj4JbC1F/IGuKlhiohA3cU3EAa4yOeXFIhizrNhg9UliecoDoSSxY1GzoWkIYTzGCvKxq7P0b0vR+sF6+9u8ab22POB6BoRRwhX5vA5rbt6ghLNoXufEZmnhcFlIoULS6lFJx2XSsgFDbbrgGLO6Mrw/TIBa/fRLpKyf2ru+3hP7kcAgHte3xK5T8erRSoxUHWtcmEZ312+m685Ap0gvp9cS9sjVpCL+05may9sCd8TsYob53C95JB2EbHKRKmnJOOcv7ckH0Bjcw6dD3DH0YQuyejyQvLBTE8bvMV/B3M9bBq/MDyE2rGwXyA0WojnpmdkT5AJv/k1RvyfmeyNuS4Km6MRfNmZYMn1yQeg4xSLnNsNDag97TTt9umfW8k5dwxz2eESgiZlhlTcWpBUG1uWN0c/HdnmUCdCYpskGzo3BCl5pUgcrcYL214AAFyz6JpgmyiF3DizF5kqPiY8QhMj5yAED69/2PiNTDqdLCqsXSLD3F2wdkmS7AdKKE6ecjIA4JoPLMBjnz9mWJr2dsk7d0XYw0WPnCeyQGYSlHdtomGFQqynVomcK4Rw4b2DyHmCIi8iJo4/MNOU6Rk/ywzf0gfXVc9dhZuXRQnB5AXFqpGMPioZDW/ewf995udmCK70TEoOpoDRlpcPf+Q8yevbNCP9Zfb9AGetnH3q4JuUJAIC1jgN+MIqbpj7RFCH9/Xjx9uiTO5nvMiQZQwbndLh2p6f16lLZ55D9AYSOdeN0uXkdQDAY5ufAAFXzoWi51plsJq4wdBYxpEhhNmYOaYG/3PIZClyrn7HBduOw5lvXhH8rhtZEYHbbQ5IsjRW2KbiDO9pRTSLUhLx/7QUqb+eKNQnLEsx1v9y5TORbaacc33OE0o96+X9RhjlcTnnuXXr0Hbb37Vmvg3GuV8PdsCnF0Tk3KRoFH+evrwLz2NYy+L5BPSc87abb4reSbvVB3/3Quz5QBSRIuoC7320GvV2SfjNdo3cNzx+5DRg3vuAyjANRHaS6RKWUovO7a6U5607ul1m8Yi9WBPcHFDoBXvrHn5PKfd9KZuCVYxDHq0K2eDxv02R/k8KPYAU+XNB0dwdTQkQa2hBrKH+KQWpC3hEZmt/56pimz8T5gUTywJcF61/vQkt119vPL7UUmq62H41Db0ErCxB5JwQtPa3os9RUwBbRqpz7ur6EObKGIFLuQNgx6v1WH3faLCCix1jwso1jl0By43nPvCEce4mEPSWINkJEzDyssuM+0RwISKSLiNH0dtvv914+JhKNa95/9ESn3OKVARRGjLcUMJcLA2rSOT8q1uB93w7PDQl31GcoZgE3w9vQuDSqC5IQLCtJ6YcjSY3L7sZ7/3He/Hef5hrwydJsdEu+HD4seH76nYb8c/2L8C1skbCxMg1DPMK9V9vWX+r+UQJ1u72h2OA0GFErsXMF67nKt9zUnnIdUJB8c/1/wQAnLh3I6aPjEdE7IlSdEUghLyfEFLj//11Qshd77K0Fxe9lJrnJhnnCdFUS42sJJVSU+6fCweVMMgdQmH5CowJCiwkKEfsCcWi+GQZpzQfPu5w5fedq+7ED176QeQ4BYole3DlyLmcfz3KkAMliaxstnXyhdvr6EBu+YrE8wYtDX4u8IwTovuOuTL9dQgBxqbMTy8iXl8ftn/7OwrsP5DR84BzbwDOuBaoHhUY5gA3G/brN7PNMhB06YSFKRwfHZQajfPfvv5bAEMTORduaSYtTJkR3CBfO+1M9Dz1bwBA3m8vBcFRe41ERdaScs6j9xnVExLZmRSDgI5KWzNpCgMsrbCEyDkA9HQUIdOKE/FNUiBdcn3pIuf6nOfpZYXEghyTc77pYx/H9m9+E14+7GcT5yQzbg+LDMY4t/1II6EAc+F2dyvPAwDdOQevbmiLvcTvn14LlzH0oAJ3e0cFhIpHvOVh/jo/XUP7bPaoKAlhhJtB+luf4+++PIrYIYxiU91yHPOhvZTtMyQW93w2hIpT2+LrlxRZCo3z8Jv3l9UDAAo+EsdknCvv3xA5h+dEHLashyufhDCs96Lvo3y85MAgBcwbV1vcOdXXHDiZnnT3RQ/MjvXySr5GC7Z24aiSr+4SW2Jrf+fC2gvbJGPFssBcFzu+9z3s/MlPzSd4bFCR8yTdRIhwBlJCcMxtx+Dgmw9Ge0uIeiiX5qgT547GkxMk8i/tE7v9FnY+qhKBFbK1samIANC+w19LPTcCHc71FuCkKLmZWrSx2XHf/QBUyHHFggVFL2NpzyNDhjsffhheb7KzvfbUU5TfpSAG4tjaZx86BshWpSYBU+4fZ5ynqPjyvjc/EzgbZaGMJqZKyPKHJX8AAGzpjiJwBitHTwhJ8OTqFc93fQRr+o9AV93+RY1zm9qKk0w4cGbXzuT7XS1tVqAGJWRMbnP4bLkeZxid4+brOsxR+u1po8O0UFlXFCmK/0mSZgb9BmOsixByJIATAPwRwHVFzvmvlxWtK9Sc8wHA2gEArWuB6jHAJG7kJsHaFcmFA094oPNWJvi7zwBN1aW5i3f4NMZ53ERZ4ytc46rGJV9AWmhkIi8l51w2Bg3wcBZDZNHZ1RfAUvsWL05ux2ClaTqPQB92eXRfvRn6P9zSdvPNaLvlFrRc/4fIvq4nnkDLC61AmRmiznTWN7EdwNjeLpUU7vVbirblR00NQGOUzEzIgEqpaX1vVdMivl1SHmhZVFlq9+FrhIXwyEBH8JU/J2Oe9B0/BKbU+/bP1SPGQ8kbKq5NqVmF+POVzw7swmKhGwBEDzDPbzqj++bLP6Xe0hA5l3POCzt9KKrEfn7gKVMG1L5BiecO2DgnlsUjXz4yYeWBB2HdWe9TjvnULYtwznXPobPfHCHrL7jBd3cRlgK84l4P3/ibb/zppe0Mufs6wkTul/r5CybWR86njIKRqEKY8SM7de2rle227TviXNk492GZ0jhfMetDAICVM88DAPRaZgeMR8wOJAc26stppO+ySp5TScAwhe6IPo8dGiPZxuewse4ybN8Q/QY1E8KoLKEICCU/Ufg84pTKxrFqlFGgz27OhmlqLiRY+2A5W4ZR7CaJAJMQuK0xETchrgsyCKRJXMpdWY47sJjnhbB26dClbz0V/F3VHfYFSgh2VIbIvlynHUFusHy0X/dqkeYp6x8I/u5q4/2EuW4kWHL9557Gc0dGWd4HKvaoUbBHhvnBW7/4Re6ckMYVNbGpAyAV4fYkx7djIJzTRY+cp9JDfZHXB0eCj5gQpWlrjcdFcdPoEKO7pxjTdAijAf+NLh25DrT1h07UyqSgWhGJyzkP2gGCU6ZlUIU+XibSF/nNJMLa4X9vCQ2RW7MGbmcnvvid5cbjA04TqV9114QBiV2bulLD2vNuHte9fl36Ki4xfdP1XFjEwn1n/gs96z4NWyKlk/vzG81vpLvPHiRpZlDRU08D8HvG2AMA0iV1/BfLbStuG5rIecdm4AsrgIse5B09qc65JLL3S0TOC9QK/t7S3mc8DwCeXtmsHJNmsozzdouJzksgrwC0yLlSpF2KnFf4C+z4aBkfQIV/KVB810H/7qyJWD3KvHK8LWxWktPC8B03/+9l5gjI2dyQ3/S0WVFeXpbFM1kLjqzh51XPe1N5DKv53ufEtnWwpdTmHjEW7Vlu1G3sDb2+dhlvaGPr0mDbA+s47JUizKUSE79w7pRRc2Rs53oOw5+6D4fGL8vwBe2GZ9dh0zY1QpzmqzsFF0uf2Vp0MVMI4YayPwnvdAmM7bIYYe1Jcx4QRszlby6jL4QnXzI035acc+bxnPESpe597wPL5ZBbtYo/o/9u82vXKsc9uYKTPnb1xxBQsXA+cxgx5q5HjHMDikUfWpY8RcZMz4s+vAgPn8NLORFQXpZIkxp/sDS0q6iknqeejBB6BsSMkgLd0jQPANDkK1i1zAwrdX0UmVwq6j1zR8MBBWVO5L2wE77ttzs8/k8fNa8dds1bAIBKA1BI5Hn6F4PXyg0ZE3v+uJn1/HpaaTSxhj5Rd3Z4z03PS2zt79zIOesPHf3djz0GZ2cyadZASqmdsW/ovDdVkmlsXSoZD26Y3iOzsMdE9cbUlWNj7RiUN/KP6+astMFZRSZvfDSyrX/RIuSWR40dh5Rh9tK3BnAXLlNu+xsm/PpXAIDMOENgw/MUHa/+Ax8I/i6bOVNqiGTAx6j7vYsWpWpTxfz5qDs3XLtLMs6l+Wn9G2GqnDFoNcjIuY4QiBPKKJimlxKQWB3kyL8diaNvCyPaFXZCUK2IOEW0AgaG67a+H0+VfRYn05flVoctTYFyk9caQin633xTuon+osV662LN1DPw6n6fU/cSkpgOK8tXnv4KfrP4N9jnxpTozwSiYItaaMiOgNc/HjYNkQ2ys7m2bPew7O9OSaMJbyGE/A7A+QAeJISUpTzvv1pOmHxC+pxzOyHnfJ/z+b+EBBGkOLZ2Weo3hlEMGhjnNuwUyrejwQa3dvTHRs8FzCVuogyM85jam+GBcWztUs75Zn+SOvG74f5aqWSCtBApxoLjwB2Acj308nYpYOq3m9c0D0eNjyn1JqSaR4QL3fF9jXpAQX6t2gQ7qnJU5JyDxxwcOa63EBr1A8o5l/reBAn2vLxjVXhMJotsvhPl/S3Btv1GHgIwzTjXS6npNY81EceJN/yt+5Zi03NqlI6mUDZeuGctnrhpOdYvieb4yxJEzi2Csz63H+YcMcC65roEsPaBGufRba4/phe8Z1J0J0JkA8uH6AS57CExwOzeFikB1l7//nODvwVMtOffT8cSwsny0BuhUarUBWYMuYIgFqPo7lPRHCyfx+Y21dlqVUeRMEKpEVdOgrULydAMxlVz44AyC7MapbrNm/h8vG9c6ovn+cZ56DRNUigb/PGaZfy97X2ImkMYRIqktv7qQ/thxph67vjQI+eUo2Vk43xSYxj9s9xwfvIc/r5en2IiWgzv17ujDLn2jNgROfbUy/bB2V88AGVl6rwZOFektndtfD14lMw7OOfcy5UGGe1/4w04LcnzmC5H+pVhALNxTjwXxGc0Z56HaX5+6cFTw/meNUdTQ2pPPz0wKKaeKBmFJTiBj137Cxz9+ndADGWd9CXdlRGAg2Csz06fjuxkjrRrv+22oMKFkNYbbgjShCb85jeoPfFEjLj8cmQnT+bOQF9kR1ac0dn829+mbteISy4J/i4tch7+veifG4K/TXqxPPfVj44PXMUFhOTI+Xmzzos9nzsb1fnopBUfxys3RFE2JsnSgcconZSq4AjSiTEkRKoEb4bQRE4GADhpyknwJMcaralJRPAyab3dMPkkdNRNV/aXQgiXsdL3DX7xGFi758AmdmCTyFUtKKG4YM4FAICdvbuHZX93SprZ4zwADwM4iTHWDqARwBeHs1H/CXLq1FOBmMh5Za8GIUpil5SMFTFBe33x9Yzt0dyosiRYu6htPqK/E/u0rDWeJ4tJ0X52tXmxFaRYcQ4113cGxEGFwgOl/fL95cjLohv5v4LE7ItrgMtfRN2ZZ/LTpGsopdQcB847oY7sYBifByH9fp593xtLAITeyESZenTyfgDHv85QpehtaidwmYs5jSo3wEvbX4pc51vPfyu8wgCiwXIelHy+K33ydvSCeE5QXokxhhuW/TqoqCAv9IQg6IPFnNMTZjciW2nj5bJ4OHga47zVJ3NzTUy2kjAJ1j5uRj2O+/AcnPSJvYvfoJiIeaYIrN0tmF+IadEXc15DjIIljHNZyVMMcaEsFBzY2bdx/JZinJ8XKoNVR0h8G9QyRrMPmNwQ2QaERGIAN85X7eT9g4Gg2mnF1raQP+L0y36Lj/xJHVc1JxwfuWZXK18TvEr+3i3ZeZwCfkgYUTWGG3gOqtNtnksIpTznnHmA52HUl74UwNrb6veKHB+wq/v10YnmVBTK44adWax+lStjZbaFjJ0B3EIkF535CiKBhyVeNJVGqabi8MhLqyEA07HBkELFAGqYQ8sqbIydXhfhygrIVaVx8toujrQB3tnGOeuLR9kBQMc992DzZz+rbMstXVbSPeSInAlFQJmDoPO5LhZMrMczVx6LDxw0EQBQ0c/QdOUvoxd2neBb3OQcD7uSj0FTb69vX6n8HrvteQBAGckj092iOJYEilwvzxc3P6aV8n15pJFWVQVopp7nnoPb3q4c1/HAg9jyBa6Gu508PWvkpz8Fe4wKxYfrBgpd7Npawpqr5JmXQIQXF5wyra/yVDRuVn18W2KanSHhenLOrHiUHmFUIbMEgMpCDTpWuREkmCmvfCApeEKmFJLXWbmcnDoPSnpKzJy9eD53QB40+iDYo8J0COa6iQpNwAMSU860FHDPQIIssjy28THs6NkR6KrCqWlRgvZcOwDubG6q4OjMzz35ubhL7bGSZkUYC+ABxtgqQshCAO8HENWw3xVFCCFKTousvOq5THFGm5MjStmJ7qd56YeW3/0u9r6jv/ZV/+RwgNkGw3h0bTxhiUlRy8fUpjzhorkYPbUWZVVmT5lgazfVrux5/nl0PvII3x8XWTfVORdKUdUIoKwa9edzdEHbrX8LriM/g9vXDy9Gue5fvhyuTlQ11HLgxZyNdOyC4b1PjHT9kzNa9j7P2ZmFNzJRCEk20E0Lg7Zaup6LCTUTosdJcvOym/HgugeT21JEZMPayoTfWTbOsWo9cuWN2Db2MP7b7yciMi57hBkDVr7EHWjFmGMra7P44NWHY6cdf1xHrzmyKMumZTzqkylPXtRMpdRmHKAiFDa81YK04nmMMw8HsPZk5TLXF6NUJBDCxZLI+PfMTpwYXkZmJhb9yXXwoasOxVmf2w9vi5RQt1lWXqsOPRRlM2ei5j0nAMRSCJyEONI2JRNHJlBiwIhqPl//j/0YAOCPn/pCsH9KZzRftH/lysi2QPy+I/e0VKSfjGJFuwTh9b9fx4YQ3mkXQqdBZtw4qV85sEeOCBTKlhHzI9cP65z71TUiqVC83c8urcPDf5DgmWKN0GHtRNTD5ff8+JFTUR7MDx4ca7t8sP9v9LmdPsOYZDTRkUgIUSLAnhQ5X+rxiOgfnNMCp8s7uc552SyOlpjwq2uN+7de+WV0PfTPQd1DyWSjBBX7q5zDhIUGk1ivJzRUBt/ggNVx/ZcE68ND3sGoHpuDVe7ClHHT2KrC0+vbV3LdImODFQqK6zlATGl6RVdLfOAkjUy97TbMXraU64+GGtXB/Qt5ODt4hJflwvXFRNL2wZnnR7ZV2qHDSRCkphJJVx1ozrksxSLnSTDqNIRwSUYiZeY0HVO77l9zf+SY3gFUq0krrC0kKHzC89e9Ge9B3gu/G40xzjNn8vJijXq5Yc9T1vcZa/6h3tPvyya+EgBY1ZeeTDktqZ5JGGP4zBOfwQl3nADHc2CR0Di3KQkcJa/ueBVd+WHW3d9GSaNx3AnAJYTMAPB7ABMBFGd++i8X4v8npK/LJxAxHRzjgVv1j7FY962wLEZ+/friN/YH35G3/yrYdMXiOyKHnTxvTGSbEFMb4yIrk/duwrlXHlg057y1vzXijdx40cXY8mm/vmUcdFXknHdJUCMtt1m85q6HH0bHP+6OtLf2yX+iIWcuM7XurPdh5UEHG/cNmZxwFXDEFW9bzrksy3ctRZ/Tly4va84ZsbuMtTJ145y5iU6AjlyHkbm/VJEXac/x8J0jvsP/lmY3W+9efn8Ttcv1/tvZ3I9fX/p4Kr6FOAIjIbkU5IsjJnKYZqYs+bsELMUJbuzlz6UrBQMAd/7wFVx3+ZNhZHjAsHZT5Nwv2xhjeBiVT1kx8A1i5rqoaSyPLdc47JIyct7wkQ+rjlZq+Yq9A1BqJJ2SI+RtkhOnIClRWYtGohYnLg3945cvUZUsAGi78a9YNnsOCgaiJyZI8qVtMtz6oiOmRM4BAMIsVGalPEsnaojMW/bn8IdthWubx99BUu3q0Dj3HWdeXtsf5+TxjXPJibu+/wC0tfrIDH9F29WdQ31lFlb1MtTM+ap2Db9yQ8p8VwaKymzyWJXnBeH8cL3QTZ2TqHuKzSFvp5TP45wANScYKpAMkciODJsSWI3qWKeeC89HDnY//njk/MoY5H39B84PliUPlP/tAY5hvmKaIefRDMZ+6ypjdZxgeBKChg99MNj+yoPrzQ0pQQKnT8IanV+9RjpB2mEwzq888Et4/SOvAwBGVPD0getOGBinszJnpyRuA8zrAwCsX9KMp/+uOhLlUmv7HBfv3BfvqazSxhlXLAi2y8Z5hmbQNdnMpk4YhUfNzmi93JueEuAxDyvbeLtrpQoVacV415owRe3qZTcEfzvCTDvqc9iYl5xWMVHwk6ecgkUfXsTbJb125roo7Aj16KY2Fd0i5l8FCi+l9FxLvonmvnTpKvesuSfVcYqMXQCc/kulbrzrubCpDdcVxrn6HZ7ZEi3r+p8iaYxzjzHmADgbwLWMsS+CR9PflQThkfNw1nzuLpEDbliETQuFr8Pkd4ZG5a5fXFP8vglQo7dmH4JuP789CUZnLIs0EAYVhLB2AMqgi9wzNnLu55xvl9gYtfqjcm6XgH95HsPRmxfjF0+a39nWL38FrTfeWKT1QyTldbvnPinkMzeehy3dW7C6bXXxgw+8OHaXyTj//JsqosPxHNCEiKOXgtAkjciG6s4NnThrxlkA1Mh5v5Y5wrTI+WBKhBQLqlZkijtCxDPoDOfr32hGf08YTTZFznURkN80snOD73mWjagEiYuA6HMGYwwb3+K5cjRurjEon3LFBVEPnRWBAA67JLC1F7ZuVX7LyiuxKIid4ZEIYiHfGTVMCzFcG47UD/abVB+JbNf2GaI2hom7f5kBXiwi59LhsjNTkHN1P/2M8nyUUTSU1xvby4Wgv0xzoPj1pOEVkN+wITHnXCiHRBjnW17WjkjoR3KdcwAPtH8dD9zW47fKBQPBqh3dKLMpMjVRZl9C+BibuiP6DhtOOTTaVli48eJkp65scO6UKp8I/5srPY+IEJ40L1ry7e0Wls/DqjOvYcXKb6UVOUJqURKJEHCnDn9fsoEh5NSXzf2q6uCDcd6BHJnDQADCfGK56PypO39EXzVFo12fZ2HTxONAa0PjrHWboVzpACUpcq60RapxLutC5fM5OoW4bmBcClK4hvKBOTrld9F+552pz0tCoC15fLPyW6QGnHDRXDSMqTKdAgDo7+VjtrqxXCmxKRvnNrUxbb8RkXMB/i7iIudLm5cqv3WUzE1LwzKMY6tKN4dMvTUnVbFx5DQPcbQWxBNOx9ozToe2Axkx78prgudh+//7ZmybxPzb9+qr4TYZzUWdQIevHZmeDK87bw6MReSSp4ADLlRqvDvMgU3twFltWwTVGR7IaCxvDHLOAaC9vz11m/YESWOcFwghHwTwEQAC21Fitv9/n/C4efT1Gr3/z0YNSJZARDXqi1+I3adbChlXUuyzZQHEfU3uYcz/y3wjLMQYBUvISXQ9hpxjnuTkXHPHRKgSHJgUOVejIhFGaaVGpoC1A1955Sbs1b4pckmWz6Pj7rux4/vpyp28uaUDr24oUj5mDxHBrr6mY42yXXeOtP3tNuS3xkdgLcPn+hf1Pa7L7gc6t/J8oQRY2VAZ50QyrC3JEK6T9CTZUHdpJjACReR8ILnuQjLamOuSvM0uGAzVpyIiIvTyOMv3OXjg10tw67dfDLaliZxX1CRPz4wxLH9+Gxw5op+SrT0+d1Ddvm5xM565fZV/6fSRcyOkzpCrvbuk7ba/Y9n3lsGJSbvte0Nmv9UUeWqB2DZ/JmqBafmoDzVdqESsy6T3IcPdPcbgeAxzxoZGwKszJGI2cbuUNMfEb8dh/WE/kY3/8Q1c8dr0iU9grVT2jYCq4fZslHTOzoTfqvHCCyWnj4vel14ODJ7pBoWZEQIwLyxr2LE+ut8kpjVCEqHE5hzXz2c2XIfydXLSruiuiuPPBS7VSxRamD6yOnqwfIQ2Rjv6CnA9htuaLsMqbzzWsRC9Rinw+v87Eb/60P76Zd52YYUCSNbMiyOTxaUum2QQOc/coiQoVyeESoRwLb+NpvXVJ9jE00dW4+7Lj4DHCP/0Mc3UI+ei3/S/FWVdd/yUibb6vZTIeuvWoTPO06bSVB8tpZ9J849VXw9A5/HwLz3QUncl5JnLUkrXEGpBMYf5649y3a5ls2r8BYYpuHFesxfBmsbFkfNJAqz9s4+rOcy3LrtV+b2iLYR4p40my+IZ5rI7WYfhSIAK41zrn93VHFXQdNFFynZFl5FefBxcPdjv94mchARg2lzJGMNHvn84zvvqQYnXkiXnxsBaYkS2E0QpNVfKOT9zhs8xBYYKqQz1l5/5ckn3eadLmhF6EYDDAHyPMbaOEDIVwF+Ht1l7vuiRcwCoPuoIiFc+u/xxnN/kk6gYPD5uLv7TJJZS0yb0C5c+FPy9lZUFbO2r+nh5HBPL4YkS5H1sXfFI+6dvXYS9vm7OOZMNMDdB8WeeB1LJ82ky46XSIa1reaRc8qZhjFaeQVpomMHIibQpH61lqxOuyPLea5/BOdc9H7t/T5KYsuUKnNjr68P2q67CmhPeE3sd2wPWjwJenqldkDHgtv8B/nQyh7UnkKYkISlKEdlQlWHhrZLunJVutW3MYYERKlJPBhc5V899tlxyiAGpNBMxTOTIufirtyNUVMPIefy1Zh+W7MnfvKwNj/1lGZ68RcohC4woc61t/f666I/Y1SZB4+KMc0NEykSaVkypGE7puPtuAMDWB9rMB1BNEZLmZmJRkEyGE3kSC1TzaOXsahRcDw2VXJmcNy5UimSj3fW48TxrdNih920IiT3/NPdU3pQi7ErzF3JlzirwbTOlOoiy06VaYhr3OjvDR2UUIyql8ohNnM1X8kWBSmOufNasEObvOSDZTFjnXGuq73Lixrv/SuNyzmXZsb4Tv37u/9DZVxUL8+zdnkHDpg44HgMhRFE4F05cyK9MVYh+5SGHhHe1KDBGJV1koEZWcVn03XnHg+N52FB7IN6T/7ECa6eEoK4y844khmP5fHx+sfwhBzFOqWacy6zggJ9zrtcmZwzP3L4SI7sn4p5Dk9+bTQk8EBACeAUz03Xk+obrjBid4eU4pWNLyb0uRdKUzAWA8r1CckUi9R/qOzPkwEew3g3QOJfn7MzY9BHjYulhfd3hGhdUShmgwzyrESxnaQavj4umQnDj3Nxnu3Jq0Gpnn6onyymrLVIFmLRi0oT7Y1J+PmYlc/IQ20Z2ahh1z06bZj6wCJ+McE49e7gatDrw1Cm4Z+EPg981jeUoq0jvpMm7xTl3ZJH1wjeb30RzX3OAJLMpDVIzqjPVyndo7fvPCKAJKTpCGWNLAVwJYJH/ex1j7IfJZ70rBAT7bj0u+D1lnxE879CfcBrszRiRWc93nhElWundFc/gnuSh1hXeEf2hctWcB2zmAYwlpj9fesz0yN9uwsB+8I1oXqMQOXIe50Fz29sB10XTRy/EjH8/hTJposGye/m/OyV4ZvVI5XxFCzIQwkXEiRogTluM8j0YOegTQCYelrU7pLBFzbeauTUu8inT26cgh/L4/64+gwilp31D4PWMk0QkRQki6xmzD+WOpeMmHgcyJiRKe3xfzQPsupjfcCRY/2j/GoPL+ZzSFBK1vJGVoNkwxukiEkTOZSXGlO4inOhae/c/abJ0TPL3E9DLFS9I41ZUQHCSF1IvBoatz0n93eEYi3V8SHPVtIceBKmoAAwQdhaDytkdIhTvng1moic9SqHMv5aInBd45LxPVfjmdz0Lx2WY2Mj7jmyQ9xfCZ/Y8BsfzUJkNFaIJCKM1OV8ZNRIEGebtyvKoYiVKff39ksNQmbWNawxhFCOrpLl3r1PFTfx/GbbpJEnC6eMWQMvKgsi5zmrNCAUjFgjzAgK3PKuKHKPL0mc57H5jx9TYyHn7mgr0PF+GrG+4vGdOCB0XkEtC1bVp8l/+HP6Q7jt6fxHdsooa57ZmaDueB9djxlz1tPWD3w5hhUKsASpHZYO/bRtNn/xkSfdQIueEoPKAAzDtvnuDbSauArfg4fXHNuPMN69Q+EVGXH555NjWnjw8UHRu4gZrTSEKhcmV1Su/R4yPrt3trQ5aG+cq24bNOB9IhRkpVYhU+KXnpG8UwNtNxnmK2vSyw6Dxox9N3SzmJeucq18Jjd+gXOgA12Q5ct5Y3gib2sYIeU2uMVJKTYgJ9arsH+R4XVYW1e9zMcGrIy2O3PjEn2MCRLaN6Q89iDnLl2HO8mXISIz9zDOMzzghNBIpBwA7S+H5jmXPnC2fKHesinJeJYlsnG/t2YrOfGcQObcpwYXzLsS3Dv9WEEEP2jkI9vx3ohQd/YSQ0wEsBvBP//cCQsi9iSe9K6CEoqE/VAKaxovSGH7nn35seHB1lJzNrogfSIFH1LhTI66QJhHHn7g5tD1+cpGVDpEvm4+JmBUT2Tj/5/qY6HqPDwWzLGRGRWtjAwCe+F7sPYgB1m6yIX639xn+IUMDpy4qp/0E+NrW4scNo+y69lfK78/cE/PsSim74t/a9njeuTDO82JeXPlweEkDrH3fkfsGfz+24bGi90kjJrZ2m9roZaFB9YoS4WeA68LzPJAYQrhS5cunSCXjZF8RQSpYuzCoZcPabGuZFZc6KQesWDk2p2CYW3wuChPJl+n+AJDNh44/3Zjrk41zpxdwoo452ZAtmzoVZbNmBgpEr5T39nbC2lk+vde/bOZMRcEllHJltsBzzvXvWcb6UXA9ick77Cg3Pr8h+NtlLGrUydFqf84zRs4Nef2WHV32ZdggAKNRTxmFpawv/JyJm5/AuK3PoIc9BqKfF+ScOxh5xRWBce4UPNgZijFj+fdmxAIjhBvn/i2W9qoEZDqs3fNYYNQyD2FKxswTleMEPFkYyyOqw/XzlR2v8BQyH9b+2L4EbKTGciyXWfQZ4xiIseQXwI1ZxljE4C44PD2h3MBB8U6o9BknrJAPYO1Vhx+u7pS+d4BwcV2gRASApUXOAX88+cLZ2rWTAoSF+p5NxLl7jamBBwqn16zAF+wq7BitQnXrvGgkzikYnFa+cS5KFQ6ZDABCrkTOy3zj3IlGzk3Ot5rjo+UXk9pEsqXVOU9ygMtVVsQaWCy4P2luo3G7bJxb1IJNbTDDIjyidzwa+8zRf8qSbz7gtABfbqyLksg1FtFLW3vMa1GSc4jJKNEUeq8pdYgQEujYSejXOHlo3UPFDwKwraMPvXlHyTkX0pPnc0tF1kKGZnD2zLNBCcWhY0M+kDeao1wie7Kk6WFXATgYQDsAMMYWA4jBTbwrcdKyuZt7pvxOTqqlnLu1T0aOt8qiA6nRzy2pP/vs2PtU7MMh3zvH8Eja7NZQyRPGueW5gRezWMmDcl8hjCulVkw8aUK4ZZmZ5D+AMaXw3BrPlwyGXdf8MnJfITm/7u2qw48Y0H32SInR+nSjueOBB4K/k2xz6vdLy+XGuUeBb32I4opP+tf7W8hcK5g2ZZEhZ6/ueBVDIfKiL6K0GSuDnMT2/NYkScEGA3M9uMwLc86l1zRmWnECv0PPUqfAMs3gua0qh9urcihnBFhbnBCl0O/X31UADKbIuRgr8eO2vDpZaTJ6/YPIeXJ+mDzWZq36u7RdPa6sMvzu9G/vB24y1JvVxjux7EDB3/A/IdHL2wlr9/Lh+yhs2YJls+cEJS0BKHwX9eefp85hjHG2doczles5JYza3Dj3352c990uMbe7HjfqKrMWbnaODy4txAqMc4PhYMjrH78XJ4Pa5vtznlyxEx+9gZOvCcPI9M4Joyq5n6+o2W4Os1feilwmH5RSq2O+YSMRDdojRgQRUNfxQCyC6dP5c3LjnHIo+8ncEavD2vWa0i1bukFtoTQSHjm3smDnq+uMcAiIprtaFDZj2RhX7zvpPKAlpxll4huX1UqON3MpNa+/H8vn74Pma6+NGO95l0fOjcb5Ozlyni8AGf4da0/XyKdkp67j8DmLMZA01UAkkY1z03sljEXXJen3uc9IHA0+27ScVVhmU3gJuk4hE61ln7q6in9crndo56nBRs6DSKnk3BTv1lTaNo5XQDlGeiel6GvMY4nGua0Y5/71izjMp+9vDuRkLFYzg5IAAQAASURBVCnnnNh+5Lw0/VVPSdVlqHQXWZbS5P5DwGBno30ibfqDKXI+fstT6jEGpOOuH/8IlX28v+hzZxpJC2s/7OrH8cHfv2BMdxSw9qymZ9WVvXPIlodaUhHCMRZhKthNocc9V3Sjd/0bLf4k7hvn8sRbHZ1kCr3RQUKyWQ6VTJhErZoaWI2N2D6Ow9HH9oaKhuMPPNsLlynTJC1LazdXTtMY5yZj4qnN4eAvt2Mi/mLSMC3oJ3yr6H2dZgOLjwG67iZ5OwfOZfOOFjcGrq97fnuefS78keAdtXyadsvjZYdcCrw1maKlLrqYOcyJOAFkDgKTh3QgIisMIqJcZpWpaRQKugKA5yLvusZSatvXmolZhIyZVocDTp6ibNMXjY0ZD+szoiRU8WcQ3U+OTJsCoQIOrCvzRFFuk++VNeWLpYycr3+VkwRW9WxToaba2J/gG4AAkCE5YP3T0EVX7ohtG/NW307jXK4h3Lt4MQCg/a67wv1yVIoQFdVkZxS29p2vq9ESj9q87vU2jkCQGdrl/uS4HkZ1t2DEuuUhiZikPKaOnPunNIzmhshKVsAr61sDwxwIS3rpDPm12VpQRlXEBvNQ6Amv71pAVe96LFh/HY6k/rwv55xnMkGdc6fggVISlOrxKDfOwTyQOs45QrSBs2X8UeqzsRAF4HoEYC5HKOjwRv+eB0xq8H+qfTVLMygwPldQlsDLYZcFkPs41cnza3C3/f12bO9Ux1Le4cZ5xuBYeyeXUmOOE0bnNBQL0yPnYi0fRORclpomMZ6ixnn4m6BMrsDoQ4Z7R4QECLaVbJybpO7096Y6TvBSdDbHsEYOVAxGV8OHPpR4ihw5Fylt8jea3TgbAFBuGXSxUtFjJTgPPJZsnMtonrSw9rh0KTlyTgiBTW1UFqKR6iQpBmvf0Lkhcf9A5K5s2MHHmpyjAGqnRkk4UztJDAGrmq6Nyu+cXm0D3Lk5ZhdvT5rIuT6/yt8jTgQB6uubO8zGuWfWe3RJzQy/B0ia0fUWIeRDACxCyExCyLUAnit20n+76MbPiInVIJSE5WJkpWmf8yLnd6w1eHI9N5U31evuRnU7z0l8bsy8YHsIa3cCg6YYY/b8CdwzlU+R96mX+wGAXj0H0Rd5AAvoKDEt6FUaq+8xV0YOMU1OnsGZ4CUY58xgzEeOGQQb7dsl3U88Ydweyc9JyE2qPDSEDhGbv4NR7QyjOoB6bS6Up1XXcyP11IfDOJeFSMZ5vHhgrouVOzpAfPhaUiRaltrRZTjnSwdEtuvGeakiupYcmdZrrQLAs35Jxs4WVfGfdfBo7H8yR8s4heQx3TCGzy2N46ScStsPoxaJnK98ieepV/TthIzf14fG5uWhU8gifHxXjNA86BHj3DIa4kVz5YZRWEHqo/4zys4gfd6gVVWYdv99mHLHHbCqqwJCuPYX1qOvJXSqMgYwYiul1OT58+S9w1Snjj4Hf3zkBzj451/G/EkjlLYAwMVLOWGQKXLetyiM8HTu4sZDUH8ewLm/VfMYg+VFM8I+uc8nQWAhI9d81qDGLvW5KLrfDO0KMa9sex2ksjKMnBc8UIuA+jNG/QEN4IRwLDAEqRYD6K4ar/xmjEnGOeXKJ7Uj5QgFrP3r7+W5wrpD2iIWCn5VE2Gc7+qVHL7yQxKxyTzeA2SLQYnc2dWP3ryLl9ZF4dKDTasZTmGOEzCS62vg9qtC5zlz3bBEZamR8xil+yPfOxzHPnl54NRR2iUQd1qU0wTztSmJIC9Uid6/4YMfNBwXlaZPfgIA8NBvhxZWa9L1xvy/bySfZEmwc5HXLM2p3z7i2/jDiX/AyMqQO4JU8Lm/Yu/5JbXPHhmTgmgQ5rFE2182tENYe/KYMKXnAFFjMEMz6LPjjTZS7eCt0Wo1BlIE1j6cMq7goExaC1ZkM9hnykTkM51mVGNC5Lxy//2Cv5UATCDqO94w6cToEcwD8dJHzsUxIoXxw3M/XPSc+5aE6Z8m41ykfsWlEgn5xaJfFL3XniJpeuCnAcwDkANwC4AOAJ8Zxjb9R4i8OPdbPWgYU4XMpEkhrF02RGV2yfNvjr0m87xU3kqWz2Pa2iWiIQAAFwQulSLnIrWwiHFe4cPZHIPhrYtbxHjtl6Nykhev7TYfHkst5Nwczrn3HLy83Y/m6AZ1QEIkiemdmCLnCe9OUcLly0jKc2GAeffvJFk0nX94PaLNpOfU30VmdMidkK3mE+9Ji/i7WLBOfSd5qd+bcs7l/jaqMv3inlbEuEsyzgngR3gYKLOV84qJbZkXwuwgWJZ3rOtEXyc3XOXIuYl8TZSNyfepC5hlUxx21nSUVdoo5IqUQ/Nz0pWyPwGsPTlyPmEKP27krsUKeYyutL/xZFi71oZv8BcbPrZtNsR3F0eESaR7d/6T5871LZGUcEN7y2bMQMXe3CkqSqk5XarTo781A69HnVO+dOcSrN7JI6/y6/z5oyth+S/vyNnjIvuFmCLnzb+5Lvh7w5ucVXjzCu44MfXYOFj7hfMuhAULGVkRlNOjPnI08jZQVgC6gHDe3vY6//e+K0AIQcEvv9a+oxeEEtDXbwQANExcB1LmE3/5Thsd1l7VG61vbfmw9s7ucg5rpzRKBuq/F8GEfu8alTKHUoquAn/v1OPGeRyiLJgm/HntW2fMw80fD5ndwzpQ0bf7g4eWA+DRoT1JuHHOv3t2vOog6Xk2NGo67r13wJFzO8E5GgCx/U9SeRh3FovfVOvJouqLWy5HUJOnn8Gs7LE8OYOVAaT5yWs3LeNztTyn1mZrlTxdAKhYsC9obS0aPnwBShFaZQggxbWrSM65vC+tcT5t/5HG7Tpbu01ttFWG5Ke1I1TUwHXzP4/lozQnJaMB8u7kO09W9t37y8WYuevAxLYVk1OnngqbmHWJcsZQEKkSBDh3/FgwQrC2ukNZZyduehRAMqy9/gMfwPhf/BwA0PrnP0f26znm1BAwIfCCFMF/b/53wlNxETrevCa+BqYhauuVyrrKxnmFXYEL514YoMqKkXD2FIawlOHbLGnY2nsZY19jjB3k//91xtgQM1/85wkBwdJRfOHqzXbBcxlGXXEFAlh7RjIe5M47h0OpaqcYIFKuV/KEHcBlKYXjK0yW5wZqdTHG7KwtCESK38sUOZdFNs7lBaPtppsAcIfFps5NWNm2Et994bvwN6oXsaJecZNhNfL1KLNlUuQcMcZ5XjKQdnTu+d1+/zU+ZEwvGyNHyvR3YVmo2H9/AED9dD75xU2RIW8zN851JVc2zo8cf6Sy7yNzP1L8AVJKYuScedwZQVjgIU9bSi1OYRhM5PyOH74iNY2BMQbmOLFlywC1ZJy+vZhxvmtTV2Qbs9LlnNc38vFX37EGliuNZ700ljQXWH65msgcohmAcs65cq23EdYupxD1Pv8CAKCwOXQ8ZCZMBACM+nIU0QMgYGuvnKUSD61/ZCRyN/NogSilBgA/eXglgPi5tGnusQDAScwkqct1m2HtpjYRAg9MKYEmJIC196tzHfMYwLT+z7zgm1plWTgWT3chDKFxJhRlf+7vqp4YnE4pCfrG9vxecAs2CGNwO7jxSrUyR5snLMTaKaFzlkntWdF/LNC+ITFyLkQmpeTPLJH4gfNoyGlpch3rIHLu/3Hh4VNwxAwJ3SXWNcOatHx7dNwBwBEzmozb3ynCnEJgAOiEcKKWNgD0vboInQ9x0tfSc86LsGOLyNnkSbAb+fuKq0pRe9JJ+PtxWbzy2bAUaJlt4fL944l0vRTQW5OU9bfF6mTP/2PNgK4pRNdrqo48MubIUDrvuy88P8PHXlHkkcdQNmtmyQzkpRzPDLB2JX9a+pTCuVYsDmXFOID0yLlNbTBfM6kbWYGD3zs1RYsJluziAa4t3WrFm01LW3H8ajUaXCzIpQslFGOqxqBr2Q/Qt/X9yr4yxlDwX9WNtSEcvx/lyjpb1cMdDkloWkIIspMnJ7RE/SZGQjjmBc7LX772yzBwFiPCuBZOkjTR9oKEEpQRla7ngkoO12LG+VCV530nSBq29kcIIfXS7wZCyMMJp7wr4IPCpQ76rR4w4nFCjGw2gLWzvDTKDJ4lkdsmQ7RYSli7LEKJcAmF498nw9zU7Iu2f79iuelANLouoIJC+tzQ4bBsZxQCtuP7VwcDOiCR0Bd5g4FtWqT3vVktT5ejdmLOeVzkXI5sHfUjM0R8T5KVfgl5PaJNy0O2b90YIhbFpOv/gOn3/j14/VZMt3H9ufN1H1J3/RvXK/uVuvfapD2xZiKGSkz8BvfP+Q0AnyDKdQCEbO1JisaBp04J/o47bCD1iZ+9YxX+/n11ofM8huZrr8XyvefD7Ysayvscy2tV73VotMIDwI1zp4hxblIaQ+M83gHV25nH04+0A+ARzsa25QExHnv5T8DvFyLf5+DXlz4OSxrrFvHHljZMdfhpXM65KTq9u6TmPVzBr5mbTDxTNn2GcTvJcLZ23ZiWRTbEO/oKkW2KjPRrGmu64Pee/b25lFqMeEiOnHc9plZSaNnKERtLntgUbmReGCi2CBjhsHDqAURMFGJt8w+s71gdnM5h7fzbPtLxec7WDg+5ZTzCfHL9jwEAk8tC59X6KacFf9/z89eQ9cvCZUkPsOMtgFjRmsrae5nZELKA33H6HYqjkno8nV8lvvL3H3FFmHMuwV63dW/D8bcfj01dm4K5s9g6PWdsqHRfd0E0TeYdJQUnNjpHK8PoafeTT2LbV7/Kf5Sac17U0BMsYRa6Hn3UJ56LHjX1nntQdeihuPcwG7nR6pg9bb55zgSAfApiqVFf/EJkW6bQhfVr8yjko3PUooeHNi+59pRTSjq+8mCffb7I/Nn74ovoe2XoCc5k0UupnXb5PnAkHVjlWolPDUkjJuMcBLh73jU498sHqqSWMUKLEMLp8sSm0nRDhSyXqbrYZtsOIudl8tzFVCYr4XSkVUXK9RocZaO//nX/IvpzGp6beajsD9tx8cMXJ95O6HjiOxRzXPTlXby6sT34LRvYHvNgEzuwLYoZ53FptHuipJlBRzDG2sUPxlgbgGHC8fznCAEBYQSMMHjElSYf3rmc5pbwYD8avPmZBqwVbKimycFjJUXOZ46oxHFz+KfyCIVD5ci5b5wX8Wpl/IhgGr1PV4pkYxxQB91FD15ovEZgnAumbX3yMA10aZs91lwa47f7nFUk59zscSuGBtiTZHMT0FLL32dThRqtqTnu2OBv3dPe/cyzoJWVyE6cGCinOpxdiAuCDkqxsi6qCFmEKhO1DNGqzlTj/L3OL/GJQjnrs/vhIMkjLiBjn/2EhU9fwsdMewWvpcqI5edGpouc142qCI3QmO6gs7ULqR1Rjqp6cxR/8aObsGujGk1jHgvSPNyuaJ4ctSmsDI3Nt7OzxSPnQhQoIfXbeO+nYo/fuqo9bIePvpl9GB9vbNsSYOtr6OngDoWcFz6zBW5w6gZqdsoUrfFxOedvI6zdbzLNWDGhHIGtNfcfkXOOQjxjrTzHPL+WrwtyihA1jBm9H07p2m6MnI+47LLItkwZB8knGee6ktbh56vneqTvwzwwnz2NLr2TG/wekGEI1ylNMazuDlEHhBJ4Uiu2jzkU/eVN/D1PPATjssvw0ZEX4+i5rxtaChRybsCvMKXsZaBjE4+ca3M20d6LvA45noPmvmbpWAMhnHiGwz8NjOV5uXLO+f1r78fO3p24c+WdYf81rNP1PkJiVttG/Oy6T2J8F5+P3slkcIC/NmZKK+s1GLZ24/X8Dp/fsBEsl0P77bcr60d7HSfA7Z88CqfddRryXj7igE4iOn19n2htdCFlc3mZzKaPfSyyr7tmEh5/qAMP/NrcR4dU/Pmn4UPpcuHtkRz2zVLwBQ236Gzt+vqlcBClZGuPE53nJkP4uGuu34jyqkxQ4UGWrrI25Tdh1FjJqK7PDKXvLZRmFDrMCZ2C2ro42SkExvkUJWhE0N8mOezTIqUMzgixTa9rbqpzTpiHc55NvwYLm0IY58UCgJ/7+2L8f/a+O96Oonz/md095/Z+b3LTeyWNEAihht5EqtJUsIEdxYKiIIpfGwpWRBS7AiogSBXpvRNCSO89ubm9nXN2Z35/zM7uzOzsnnNbCP7y+MHcs7tnd8+Wmbc87/P+e4lUcy6xecV1irT6jIGeEHw3oxDnnBJCxooPhJBxGFiJzv8XICC+4iNDkVuKJp9KGtJGpEvvR9A6tpQgs3oNnsVcsxNAvT5FExftWIYiaVL1ArV2Xm8L5KeBpPyXIc5HlQdVPXOutzOTXxzL8K6npHq2wInTJ1gDy6Bs4UJUnXEGyg5bFBslfmj8oUgl/Na4fsau5his2fXuVYOs7QBmbuL36FfH81pUu8rPGMiOh3YNaXu7v40NtzfZ6LqtsgJHjBuN6wzBXMYYljcvx/I9y/lhpMDQopGL+h0pB3h7KJmuJqLSW+sJdtby/eYsPrF5TjFyvTlcsPZCjOrkmWjZEJgwVxUhXPXSziDaLmq+dcTR2mtGlKG0Mn+LGgHqsUDg0DMYVZ5LYx1zgAcZqEfx1B2r8ORtK5V17Xt60LozNCJkJ4aS/Oco0xCFsFfYmsdc/rLgqBqQrDlzroM4KTMF8x3qc97+0EPYc/OvAfDfVfdRnjGoOuOMcKNAACvmnvg152zJP2KPIzvip/hCcLLDfvymV6SN/eujGXQ2o7ANTVRIURgkEQyQirpiP3NOIsaOUMPd+d3v8v3W1PDDmWwz6gb31AaFQ4HKHp49D66HNn7LCv+WRbCuV61/BYDUMUcgc8K3AABldgvYossNB+d47k6eiQ+MSiuaOddp7bKxqBvyFuOX1qVuIIQpt6MjvuaEbMQG4qqgoF1+zaN0XatKuJE6oZ4Pisdt447cITuX++ew7zvnCrVfAu0Ox5PUyJHhikFSa5eOBADYOYyzDFr/eSfWvLorWPvagVcAAL7x7DewyVeg3tOzR91F/RQUirKqcDwc/9e/YsrzyRrIW1e2Bn/PONycIBgohEPVeM01mPamORhQJbXZjVPYl9H9+uuDd4IJoEwVPdSD4UoL0QJrzguFsAWEs23KnPem1HmdxLRKLHJLIsuAvrcYo4xKY496Pgf2ZiAs5SwsnLDqEoxonwTAQm9z6JzrQcdYmAJlQaKK/8Z0ltt420dG2wwTRmH3IT4eOOd2YZnzNza3Kp/belT2nk1sfOkfS/y/8zjnQyAy/E6hkBH06wCeIYT8mRDyFwBPAfja0J7Wux88KsYz55WZOnT6ES+hGOq1tIYbaxndy9jX8ZnM5/ztQ7A+1pxXdLcHmWcGglyg1h4K+eQbVBx/IPv9s+uN62U7SM8y6/Xsk2tC6qdlMNRJcXHg7AcGlHxths8OaZ3y99JpjPzB95EaNy6SeXt77pH4xLFfNJ678jvinHPtN3Vm3r01LaVZoMq3pYaXDg8NSUARvoqUCQjngVjobVGNNFcbQW6uUemBYyrG4PAqbhRR/2n+7GOf5Z9lxkOMOEp/oRvdAOD5fUQpsdG6J4u6rmE4fAdvzyQbDnZK/VHb17TmVVM2CcLVl6dBCOmTyj9jLHxnDTXn3DmPPxfLIqAUWPr4Frz15FZ0t4fP9Z+//jz++s0Xgs/yPEethBp9sY10PoR5gGUFr+fSLk431p+d4u9dirX3D/PXJe+f2Da85mbkduxQlr9Tau1bP/+F4O+ute1ouf0OAKoQUtCmKMZoEK3UmpaVG9cD6hha4dO05bG0RNIBaJKCBXq0w0RrZ5nwu8Vl4btLCc8S68+m7iR5LS1gnhcEmGYvHh2ufPm3QebcthiOW8L3VdeGMNhX5Lf+8RlRluQY5zJeUHMu4z2vXorTnw9NjNJKs2NYUiH9HvjjB7HQoXUyiGTOpXmpoUTNhFmU15xnvWxYYiGPJf577vkCf89vex6/XsLvCRiw4f2c/UOkeesLx/Px73WfunnUdC6wGfSnfxdkzuNo7V5ra/A3kdoI9qUHNlB45nz5jIvhWWkUTRgfsDlkyP2n715zt7py2IyCzuX4Lb/ARdctCj5bJSVw/CDV+dcckvf7ZVX5x9J+QXqm4q5v0aRJ4Qd/m6Txc+MFya3ZBguc1k5wxPv5u9AwVm0JJidyCm2lJqALvOmIOuf590uYed52qPneFtJiTN8+sHk0ZfgSypAlPKSYpaWYtOdAnLziY0hRTfiQUZTMVfUzTDB2QRLL/LEnm45vNddn59y/Fmmr8JpzGfcu2ax8togVlJfmGycCxu3/AAoRhHsIwHwAdwC4HcBBjLH9Nef5QPwXPFJo6df0PfKEuuwj/wk+upSGFEV5gKC0oJpzy8+G9pRVKuzEUK3dQ5fb4e8y+a0TKqq7OsxCUXGtgPjvUA2vFc0rgr9NLztxnMBwCgwo2TA6MFlNVFBIc9u3B8u6K+uwsXKEsbesjOZbf2dc7moO0qbmd2dNS4+UGP3KwV/B7p/+FCsPWhCILynUYW2iCbIjxIZT3DeK8bcO+1bEaRDRTXnQNjnTA4FJIZQSfjxG7AjVT47SN05QAwyEkLwTum1Yf8y0YVwluA+XjHoscGxMmfP1b+xGT0d8dHj72jZsXRlS9O792Rux2yrlbMgfHJFbBVnUU9gWLd5of59RSjHNidKYPAZ4ygHt7MSaxbzEovJUXmO5t51z2tuLVYer4ktelwvXDxooz05QrWQel4njAJ6HnqYEZoJ0yYSjJsbSKcPKUZwO7033Cy+E39Eup5gz5Gw5yxrGbb9c10KUEWUyflg2GxxrwhyVVRLQ2nV1OWH8HXgR/9d3jMb+9jfBJp0tmaDNnoysA2zvDbOeqRLztZt+6AiMmMTf1bW9fsbHcrD29d3alvGZc728R9Das14Wjq/CbZWHNCDhFG1+mP++Sx+5FL2+MKLdJQUFpHdD7/LhpHlQwQ569xp/3j4DWRAucTs5wN1HbZykFknj//F31JwfljxRy0bbPfdi2Phoz+e8bZtMWQENJSPqkUqb56O6kfFBNgE51rJrY3ve7QuF4mTFXF+F5SHa3+Xe+YQCp7UDc48dg0/ffGygFRGul/4WLfIKeDEu+vaheP9VByduEzjn/o3ZvDzaylCHxcyZ86PXmUvv+uqACro27yKgPmtpMDBC4AFwfeYRJRSf3rhY2Y6AYdiX8ieeTAm9wI/wr3V1CxciNQnNWsyD04efJ65FXwThZOQ0v2GNxPbL55xPq4km796tKEQQ7iwAOcbYfYyx+wC4hJAzh/zM3uUg/v90wyCkw0k1Ntkslp94SfDZoyyIqucUoQyvoElv9M9+BgDIlqoTiXjRbeqhI8cN+Di19oPG8UhxXC1tsE+5L3OMc/7Fg6IDiInWThwHd6/m0e6OrF+LK9MiN0UV2NXvc+d8zYknBcvEdUzZlrGeRqDnjTeUz7SnB9u+dhVyzepA/rnb9g4NbLAhB10/OPODAV03gFzXGhewIRYqxqjZCr0+c3xWdRxLndKIcy4Ga9mRi9QHDhCmTPzYKi44Ry07UscsR+nnHDtaWXfkeVPzGgp69uuSw8bj/86aDWKZI/Bx2XRGEThCegCBUZbomJtgyi6ZIL+6zHXzKqQT5hmzxYn1vvnsYs2IsMr88WsvO+fZjZvg7dkTu572StdUOMSxNef5nRq5IwTRnPPStI2M3ObQE+8OiVx+kV2UM5iKar7ECBGCcMdtegXXPn9rsNyUxWW5HDqff5Gv14JQLat9FkGPltURwTah1u63VCtfMF/ZrsxSaz0BIKsnym2zc84A1I7SnCXLjrQg1HtkJ5VyzV/HMHk7z8A0fvtbGHn9D1FywAHK/uNQsbFJ2i68TnpgWDj4Ipiyr9PakXMLeo5daa4czMx5yezZKBoXVFaitYoz8Exq3fnK9EYtij5vJQepgnyjrv9h4j7y4a2nQnVvpV3lQCHZfrElYHKfc/+eFdLtouqM9w7s3PJArzk3rRcQDK2k5joC1cNKUVRqZtYI6Jnz4tL8zzKBZaRjWzH9z/vqgGa8DCxi8fFW22fKF2fNEoIVKT6Wk5jAtl1fb1wuw5jQ08axpRN4EKmmMdoez6I5vDy18DFKF4Trs3OulWG0doef8/U5L0/nD569W1BIePObjLE28cEXh/vmkJ3R/whkQTh1BX+4ZINBV8at7GwNnErFoPVoQbVcIsJa0tul0Otc/4VMSS9LHB3nDx8+GE9+eXGg1h4HT8oK6BRwMVGmDO3PjDQZx46qLcoj9MoHEs+FpDiFVG4FJvoD99UAavvXv9B2993I/eZX+TfehyHUdGVxE5PzrWQn45xzywbRMg8/PlsTdtEus23ZEVppW4YPJ/Kg/fy25MBLX2HKnHvg5RyMOGhrUScA2emQDZ9J84dh+qJGpVbbBP3xGlVdgrTjR8YNTun6JU3RheBMFvHOUs05f1PqHV4o8im3B8eV3uN1p70HK2bPSdw+qOPVKyB0pX8l6JMnc67VtQonc28LGjXdnPzOs4yUIcxHa08lG446xHMk6tAti8CV4xv+e9q8ojxyPU9bz98h2hZM16DdXdh9001guRzGz67HqKnVmHPsGJQzgnJK8KXXbsdCv/YZMI+TTb/8JXbddDM/H9nRrB6H9k18fKFdujaIv51mYeslIweUPhQ5XqSdT0yfXC/rgbo0sm2kHEQbf8S484EZIRNr9nqK46Uy3qyXhV1ejioh0Oqje0O85shDG0P2myyIVppWz1+MLwRcwXogWht7A+7u3ehdtSrvdgqlerAF4aRr1FExFm2V4/HCPesi2+VzBCov/nJk2YjrrkPjjrDcR24PZ8KE9f9OXC8HUOWyov5g2pI3wg8FeKvu7pA1ItgOzM0f0C2eNbvP59YXUBqlqR929mRpvZSEksa+wYAI/M+o4+ydifPy61kTZhkDPRYNn+vFYxbj2kXXAgDGVYyL3df96+6PCMa9vONlLG1ayq0yzTlfUcTr2rc4Du6s5OyQYs8o4lOY+pdmww/70helQAnfQVcRf0ZMYrKEumiqLPxeCJ+i0JpzHesyKjGbSImbfM/E79/6fZ+OtS+jEOfctM3gFoj+DyKoOY/NnIcPbPOf/qxs85eHrzP3rGW0MBVUf5uz7/mFYjQK59yWHPK4yayiOIVxdWWwLYKJDWVoqDDX2uQkR07PnIt9m3pOm9hlVroIi0bwWq/6Ej8iKA8sebKrplZMnj/QORYxCuDH78x3kFryU6D2ZQSXWXoO5DrBALJDHpep9GlYMnbUaMIu2ldsYmNmuta4O3nQ3tWzy7hNf2HKxFNGYVmc1p7p1bJrMQbyyZfO4m0RdScgD8TuCCHGVn+9XWaDyXNZ4Jx4OfU+9HYOndiJXN6S3bjRHFHQ4boRanWUxqlmzlNl8Vmclr/9TflsiQxwH+v5BorMqtWJ65kU2Q8YELE0Uz5VFg3nTmxJg0Yz97+f8nKoynTgry9uwtfuWhqMpa9LLWYAwGsyB3UA4L3rn40sa739DjT97Odo/uMfUV5ThDOvmI+qBj4mTnKj74hJcCe3cxeY/z4pYkrUA3xnvTilvU9insrjUFRM6b9TsPTJrUFQqcr21X6JDUtje+mCcC5zMbt+Nq48JOxNf/XtFJc+EN7XOGGh0gk1secjzy8ZyZk9ZrrqDIjg6Flrntrnldqp3+++d8mbebftXRqWvRjrXBOQLyMmjzMVHZvx6vwvGwOmYyvGRpYpOCraDi09fhwq5uev3RUYv/FhHPV0AXRiFFbfnPh9qUSlkGvqtYeBuaAUoQDmUSHMiIHA1Of8wBPH4uLvHQYA2LUx7FoSaAEP0rtRmirFb0/8LX5+LG+vKw9Jf1jw9eDvw68cjmp/nCaMGI9vsfA6VW4dhVFsPD/nGC95WdMyfPXpr+I7L3zHuJ4QAJY61jzo3/IXSophsSSblxVWM6fPTcSKBM88v6Wl6Z2ymFdINUi4L9/ut4ntK63zz1O/8SA+a2Ce6le5mapCto50rnnHCQD/XPVPbOvclne7fR2FjKCvEEJuIIRM8v+7AcDQNkX8HwGBmjlnNBR7kjPnzGB8isy5bFgwjxZEa5cHcauUG2KPjzlQUmsPX+g4WruM2aOqUBZTg+UVQGs3Oec6oQAAGr/9rWCQC9rbyCPpgg8nnqcpS/X20Zyu1daTS6S1R+BfQ/L0E5FVb25pLXw/7yB23XAjmF8v/rcLGnH3IpGWiz5vLEkQToCQyIjRrrGgNmn3wLZsfLBkHK7fFXUq+iqi0hcYa85955xaViSbrxtRp31qDhadJWWC+jA5AVJ2zCLobs/il594DL/8xGN59/fyfeuxpu4odJc0KBnjSO/mQQY1iM/p0EV8wBiKSvi4UNGxEQCwRVItBrTMOQMsJ+E4GmODlLwzmfN8kNlIYT1AzNjiG8ipiVNhF3sRzQYxvn/7+Vtx+4PfAgDc9tImZSyVM8m5bdzoKBvRC7em8Ozkrh/9uKDtTNMLsSy8OYe3ZOtdIhlXNIf6YycAAKrGqeUTGSZaYcbPV5MXDAPWPZH/pBKoiitf9HUAxMBk2RgzUwsGivZz/vPlUjdvGU3GM2uslM+Mz7rFBX9ry9KYPCz8DaSXj8nlbu+gZQeHCsx3zuVOKnZDfiptXzPn+a6D7ChZMTbLr082ZzvzgVgW0pMn599QbA8Gx+vNvyGA2pF5elD3ATrjrfHaa9FwxRXqucnvm+gsUEDN+VCPsUIQTodw2JdJpQCDrdYOAAtHLESV38tenutllfay6jQWX8RrlgkspK20YqN8bPbHkPJCW3bks4dgya95MPpHr/yIi0hqEEzQ7V3bI+sA/lwTSx07r+zkx5yRzcbS6Pk5hu3ykhApMbGtgNlU2b4BALC5mutBmWLyFnWN9nocdOecMorNzd3IulRpmVYoZFvONE5cOJ2LGg4rGYYxFWPwree/pehbvVtRiHP+WQBZcEG4OwBkAMQ3hdwPAP5LpwnCUcrA/MFTcbpz0Si9Y3JcqFeQIJyqLsv//vXsMwK19kXb3wpWF+Ig2RaJUNYFZEE4V5s83t7zNgBgfdv66Cn6u1sZzvlIDR8ezeTLRtT09ySep8k5X9/Ory1lwJkHhgfbVJFMbfL2xGfMt7UWNjG/09hzyy0AgOrzzsOqKaUYPnEWgJia8gRBuNG/uin4m0ij9BOzCbpK8gilERvlKx7AyV3RiGxTb+iwlzjmFiX9hSnz5TEPuRzQXDMDNKNOpLoPMX5OPeafFE9V01GS0uqlg8w5kO0JjaMmvxWbXhcrY13N4Xhh4bWgklHl5QrL3I+YXJV/IwPcbP79R5xzAMTzUNq9EyXdu0FdgqkHqZlFpd63T9QVwCrynfN3qJVaHIpmTJc+JRuSgShTNgdCgMaD2tT1/vWZ17RGWa4459o+M372hg5B1tU2aCTI40XXfyXKIXVhFfkq7Lb6nc1dvrGttATIz1aZssAwLpfnN0CpyGhZdlT3QLA3/KCkRz1FgPI7h0ezWhnX7Jwnsbf0N+iJLy3GI184CrZF1IyPdHr7fObcHyfrPv7xYNnEe+/F+L/fkfi9wc6cy5eJxQR8XBvY4DsbfUVsQNqAfMEJuZVaIUHPgqE9KzXnn4f6Sz+ubiPXpfeh5pxlCrdpRv/qJoy+6ab8G8r7Z+Y4nWkMoIFz3qdDFIy4jLxN7GAct5gFj3lKkG5U+Shsqnlb+Y7bzc91Tesa/GNVtF2mKCeMy6wfMLISXo9qZ4zoDI+ZlDnvve5y2JXxKuuPr9zFuwtpPgOxrMBhr+zcDPqRt7Ghdqmyjay7Q5gXcc6T/IbAObdsuNTFrW/dipufXBu7fT6kpPM3jRNfW/g1LL14KcrT5WFJxFA9PHsRhai1dzHGvsoYW+D/9zXG2CCqXPxvwoLFX0zCQP1pmw86Qu1JmsoNznmRFIXbtIc7NoW2UlMmRs+D3dAA13ICtfYTNod9cwsRa3AsEsmKZ32ar7xc9/n+uvyvAIDqouroOfpfy8kZS8eJCmXJgYY80XgTNevZNaGwkyNd8y8dqcaXimfOVD7v/slPYo+zs/3d4ZwLdD7xBDzmgYlBzkRzixGEq7vsMlQcc0y4nXS79Ky5CTaxgcpRxnVyfVCpU8DO+gDTcy3YGN1lI9D6r3uVdXkF3xLalwF8wpeFn8QkohsCK14wR9BNkKn0Xo5i+Pj4iVhgyoLhBe9fBqMMWPQZIBWf6Xn7mWjU2+vsBGEUzLKR7bQjz5YShGSFseWD7/qZ870tCBd3kmHfYKntT4+f9YhtpeYbyNkciMXgFFOkK0Jj2Yox2lTnXN33rMzv4FIbFAS9Bj2PQjBiajW22NHrahESud6KeKLU1xqeG1JQNd4jNb1PflZT9FsHANRMwAElocNfKrWh6kshief4mWnLiThEIgAi6vU95inZmHGVknFcWowXppH4eVH63u5uSRWeMZz/lHrG4+vLMGU4D2gpNdXS+Lq3xeBoTw+6Xy2c+Mhy3A6ROwA4NTUokjLNRTMMLcoGO3MurWfEQlEmKuzm2sBflv8l+HzlwVdGtomDcAgPOyd/Bn3ivfdi2FevRE2LOTs366hwvqPu0DKektCXmvPul14ueL8VxxyDimOPyb+hhLjMuem+i0DJULFK4sRYLWIFxySMjwG9EkOiyC7C9kruZGbSURfo+y99P/j74gcvxvde/F7wm+OO+ftLDsYJM0Yoy4g/Tq1Kp5Qadx1JHW7uem0LPvz7l/Glvy+JZs6JpUQ+blmuiQNDHaMJjdLakxi3L2zj+g3tmbDEbUVb/8nWPV54rZPaTrZmWrGlk+vykL6wZPdRFKLW/jgh5DH9v0J2Tgg5mRCykhCyhhDy1YTtziGEMELIgr6c/L4MwpWnwEDxwrh7AADMY6i5+GK+XlFrjw6eJ2x82d8O+MLf3+ALKS2M6iO9jMyjwcvpGqJJhWXOLSVz/tBb2zH1Gw9i+fZ2pU2Mnjlf2cJrR+YOi9ZziZddLnsklmXInPeh5jyP+FLH/LB3KdUefdqdLPgl4+nV8XWf+yLcnTuxq3sXXMLvj1kQTs6ch39XnnSish35YmiQeBbwkVY1E6jDsRze67hhOspTIbVTn6wGO3Mu01avO/w6AGqNu54pyWcIiPULz5gYu83kYWFm2fYDIfpcsuS/vIdnIU6qnDl3c15B1PYB1Tj2tAI5c9w17tju7t0gzAMjFra/WA2a4Ej3URcGVjF/JnRl/aFGqrHRuDxQSpfGzN033AgAyG3fYfwO8dtm0WzOyHzXlcTFgxHHVAKAHBz0sjQXEzM8SA2f/3zsdwVsh4ePdViERDJtHS+GhjttlRhF1EUwXGu7oqagg++cC1G4XOsegHqYVvJEsIn8vshP0uHnTkbdKDVwVF5bhOETeMDKE0YssQ2GsP9ZypzL3RyclrDeFWVl6CpOCFpLyzd3hP14565nmL0x/p45UsCcVYbslr2dON921VXYeNEHkNu5s6DtWYZn8cRzHECyMSLrEKNrkoC+Zc5t4wDqWcDi0YuDzxOr4sdqHYwyVNQW48AT8tSsgwcnqs88E2M3PWJcLweHvEEYu8qPO67wjaWxKag5LyBzntkQZTcOJuLU2olhvhK3diC09qsWXoVfnxB1Ov09B3+NKg8DKby1mRBr5LXSvW7onE+tmYrDN5wNACjKJpcrvLbrNfxtxd+CAF7GywRsOXl8qi5NY/E0lS1U5IclHy4rTcycEy9+3RV/XwIAWLu7Mxoos0heZkuXG9L9LUPmPKl85Na3ePePZXuWBctWkR+DOMm2osC8atXmBClMrb25V+oWsY8zkgpBIbn/LwH4sv/f1QDeAPBK0hcAgHCJvV8COAXATAAXEEJmGrarAHA5gBcLPut3AUQrNUYYKAkz51VnnsU3kGvODZnz0V2hA+h6klNVQERaob57IRXeNdThFlJzrmfO7/XrRj72x1cUh1zPrguYanFEKzXZOW/3uqLKjrJDnofSn885t0tL8ccZJ+Pm2WfA0/aV3bAh8bsVReG1++/ywgyboQDNZpHbujX/hhoyXgZvNvuDpcmBkmvOpfWkWHWaSXlYz+nawBdakgdcm9jcK7Mc/OfcUM04S7M4f9r5wWcR8RwsyLSmMyefGVmvK0LnG8zF+iQn/ucXHBhkwsTcZzIwlj6xBbs3dUSW65Az51tXtRZEkeyvc84YA974S+z61S+FzufYTeF9XH/GmYFz3tuSVhzpCp1iykifavetIHPugjGGpl/fgtyuwRUONKF88dHmFeIdkX6jGDeM/cQRGsi9K9eD5kQWJVyvtxkUmXQRDPniCVONHhzzSVgd6SjjJD1hgvn8Jdg2gVL+759H2rEizjntDAM27uZN6Hz6GX9FDozyrLl+iqUPPY91rZqatl9qsnkZN1g3rHYB6oLIbrgsXCn9Pe/4sRiv9VivbSwLgmzM8+fQPWsMmXN//vTHtRzNKVkn+4xLw427e8BIgnPeFo69pZ3hdSqNYcELyEalN5xnyl5onLnXM+e9b/E5QNSS54OoRSa2ajvI46Vp3m29I5n2rsMOmEbm9fLxGIixVRu1woQAAFh96LUu+nAXCru6GsO/Z2655mYlrZDBpLUXAKW+XNScFxIgGOLTpJSZ9SwM9zuoOR+Ag3XB9Atw2MjDjOvEbsuqi/DdI74bLC9xSoJnIMic+8759UddX1C7V8YYfvmJx3DOm1x48KpnruIrltXgV59+AjvWtUUo7vp+UyJAC5JYc965Jb/tThmLOOLEigrCAcC62iXB3yfcdUK4PYvWnCc553HUcmJH27pmXA/b2tSx6KX1qk1ZIbF2Ch0v/7/InDPGXpX+e5YxdgWAxQXs+xAAaxhj6xhjWQC3AzjDsN11AH4A4N3FFc4DueacCefcY8GD69RUB9vGUV4EgiyK5xXUSk3OCrbdc08gIuRqo+OpL1Gk1uen2doWCQIEANDazQ2hra09cOUocYxz3lgazUQFtHZpzt/dvTs5cx7TUieAk7zesQlun3Y87pl0JGgfa1L++NFD+rT9UGH7V7+KNccdbwzo5IPodW6arBVRQuU+avdUMorcAgZKm1jAivuAnhZUpMPMcsbLDGl0M27fvU4HinqbsbNMrWNNFRuMva4u7PrpT8Gy2YIMt8nDynHGvJEA4jPnAPDU7auw/Ln8711vd3ifXnt4o6KoHgcrZnzIN8bky+TvWB9S1Cavuwelh4TvA2E0UPNm0qRtae8yP0bh91wY/cz10Pv229h9443Y9pXCqar9RV5xJOldqf80L48pPzrGoZfGJC/Dr1H9AWFgJqUZOUTLnKcdC5aJ3eT3Of/KkZ+KrAqCGvJ+fQMnt2MHtl99DWxbnfzl7iD62KLU+GZ6sFnUH1Of1m6Q8q3oBc64R5vu/d9BN74k7dyDRaRgYEzmHFADXWWdW0EpC4IYAfW/a1e0flhcUzeL3731O3S73fGGdm8GlCQwyuQT7A6NTSvPqyk754K5REHQ1jN0HRiMEGNIoX3IxXXQxxW5ttmOzru1H/1In05L1N6PrY0pb5Iue3raNGSdaOZy4g6mCG/F3WMRvLr1gNMw4vvfAxCf2U1EmbnMSJ57+trlwwg/qFTIfFl8QJj/Ek5ZIZodVvngCdeZYFJrB8zB7s3Lm2PXDSYsi6CmONRIaShtUDLnLnXR4/F3vNgpTqSRC2x4kyfVGrrGAAi1b45c/z4AwMa39gQ2rmB2pLT3Jy3GK0ISM+cVw9J5z4cB0XedkEAUxyoP2YxUGoflvxmsyBAf180CAFr8kpMIG5JEvyN8CcNZB1jXFGbxC7UZ/7+oOSeE1Er/1RNCTgJQiOrQKACbpc9b/GXyvucDGMMYuz/POVxKCHmFEPLKbqmP474Mnjf323ERQYcMjQkU0lfah3B6WaGt1GL4o65E5QNjuORRiilfuiXv7mwtc/7qxrDeS6ZfxjnnI8tHBn/XFvPMq3DOXZm1TqJiRH1qpWaI4E+RVHLlmmBPenmLZ82CM1Kt+9EhG1epAbZHGQg6Hn8CgFTrCj7xff7xz+OJzU8YvyPaIR88cqG/wJQ5l647TXg2pfsxc/is2HpyAXunn61vV7P9WS/bL2XdQiEip/oE0VS6HZniWnQ2HqEsd1LRobDplt9gz69uRss//4kDjhyF8toiTDk4uaZbvAOBLTsAA6P1uZCg1LytK8jCHHTKuLivxGbO89Lo86x/60n1/pUdfnjwN6Fe6JxLgR8iOees8SCAEaXeWse4v/1VXWBZgGWBUQ8sy9k3tKfw8pP+Ip8xK5z33M6dYZbZ4KAAErVUYMZ7UT0hfHdP2fACGrtCXQyRSfcog20R5DyK7lS05IPHORi2l9VHBKqskuj2laedBgBY957T0fqPfyDzxmuQR1O59r3tnnuU7/YWh2yZIFBAKcAomBfGT0cuytN20n/fldZm1EXPxHOCj7Lx5WqPsmysWzQHRhmaNnPDjYkME7FiBeEeXvMAbnz1Rmxs32js5gAA8DwwwktgXt35Ku5efXfsz1ndFJb45FMzfnG9dG1EZgks9r3MrF2LHd/9rlm8cwAI91fYuCTe50imWnHOo/Nyemx+ergMx7Zw00Xzcceli4zrlcy5nQIl0funX8q4e1x1Bg8aPTRuIarPPBOAuQ93PsQFPIvLQzuEDoJz3vnEE/zfp57Ou231eeeh+n3vw9g//TF0ygrInNdLgn9DAUbNjpWjdQFilGH9Eu7gDkZJgAnpYv5cNE6qQspSbUYRQCCMq4yLzHmRXQSHOFhdl1w7veTRzYnrX3lgQ/DcnD7pdAB+AkM+P3+8Wl6UNjrnxKc81Y3Lr9OzbneX4bqbmSciiaj/TcD6lDkX+MCMD+Tdxgii3vclW/jY+eWTphW8i62dfWeX7msoJLzwKjiN/VUAzwP4IoCPDvTAhPd8uMHfXyIYY7cIQbqGAloH7AsIhCA0Wnswg3g5YMxC4LDPGi3nnaU8okcYAxXrC2ylVjTRXGslZ87LwDMpVi5PlgjcMZWd8IwsVCW3ZZO2kV9eeXAQE6ap5tylbrJaez5BOCfqnMu1fo70+2WlY7uyMu8EJtNpTpmV7MgPJcSgKjvnHvPw6KZH8dnHPmv8Tta3UUr9lkRGmpvkkMsGoW7oy/dy/qiFeb0+597PKZ/nNnD9gYyXGVLnXOCIUaoTTi3zMW0n+l7t+TWvWaOdXaisL8HF3z0cFbXRrKQMN3DO+f6SBEzyobtZrf8WzvnMI0aaNufHi3XO1ft07IdUEaeNbzUBwwvvOU3SYdSeMAoaZM7DZ6e9YnzwN6X83SwbnkHV4rlo/Na3IvssnT8fk598Qj4Kf95dL3jOyN6IiOcRoBPv3pqjFwfPSKxae0rLbgiHwd+82M0GbTOBMIPt+s75P17dEtaVV1ahaMpknDFvJJifOQei7XRMQcq2u+7i597pdwvYvg0pabyW6fUdDz2sfHd746HhvsX47GdOBK0dANLlMdftSH+K96nn23NSdRt1kZINdOkyPhMZe8K/LeoqOghhK7UUejrVbIy4fr25MLDjGJy74JR8WvslD12Ca567Rl0pBUq2/eKn0jGi+4llq/j3+NAdb5vXA9hy+eVo+dOfkd2wMXabfkEEkwoVgBDzQqRXsnQzbFtptWbcvgCcOnsEGqvM46syrsUFVix1XIzLntVddinee/r30CmVhMRldpMQp8NRN6o8COJ2dwweM0K8u0kghGDEdd9G2SGHhA5YAbpCJXOjukCDiUKZCc/dFXatKK3MnxnuD4rLUzjvG4fg2A9NNzjn/F+LEbjMDZzzEqcEtmUrrdcAwCmyMGfbYhyx7lwAhWnJ7NzEadsigeBojnJamQ+iNm/pATn87uCvoma02Tn/7gPLlc85QyBOsKPkZ4oqznn4Q4p79yQ65+vb1uPyxy6PlK+WpvTzK6x2gkSkQPn3ilMFJCZ9xCWq3k0ohNY+gTE20f93CmPsRMbYMwXseyuAMdLn0f4ygQoAswA8QQjZAOBQAPf+r4jCWbB8WjuVaO00jOq7LvDR/wAnfsf4Rhf5howFFvYKpp4x4qWDpNO4/aCzIss9ybmt6C68yMi2SBgg0CALwnnSNk9teSo8H2lwEINh4JxL82yRXWSoOR88QTjZwVaominH2GseAHrmHARAc+z7Ijk9CMht3Yod3/42mOuGzo/kQORT3L//YD8a7MRP1kqGxv972Je+GDW6JNRXDAfmnofrdzXhizMuMW5jt2xQPl804yIA3DmXz/ujswYc71MQtDDx79W0Gh519Yj5WlkG51xAKBYXgkn1nB7YWMmNzIEw96lmOAjn3E4obdFp7VMP4Uaink20U+qJrXp5J7C4cMo4KZKdcy94n+jO0DCgdrhNrtOvpXYJRjY+iJqzTjPuNzVcYiYQALbNa4XF89kPo7+vYPnU4Y3NYM3nFcmcC3V1sQsCVGUk2p6oOWcMjj/uWr6xYo8dC5rN4sx5o+Ds8WD7/XBH33hj3nMpXaBOq4S5KJKeFRLTrQFQx0ria5RkN2/i6zwGUloZ/BYjavwaeNM4RamikSK/L1u07hvEkDkPztE3ZbpQj2f/qbalExebSnT9JIpqIq1dcgyPWiYHNwzbxj1HBWQERVCnL0KlhcD1mYedTzxZ0PbiXdDtDqXm3LbhNmusiUGeIuXpmsWwVFL2POVzXACGEIKc1uUgTk08CUntLY/5IG+3+PJ96/u0TxNKFnAbpOqsqE2XCP8ZTyrTsauqUHPRRUiNjA/4Dgbias4BYNTU6uBvudxrKMve6keXw0nZSNtqACBQa/cF4Va1rALA7Vab2CBaO1A3Q3HYxrMwa+eRWDRiEXpjtEdk3PsD3rZMlF04kl17845deZ1zq85F1umJBBYEbnlK1frwDC1MaVdU+FV0lRLtWHeWbwDAh/WIWrvknH/7+W/jsc2PYcnuJco2juXgyFFHhgsMEUyjKa1tt2A8P5+TZ5mFWk0YVprcKvndgFhLhxByMCGkUfr8IULIPYSQnxFCauO+J+FlAFMIIRMIIWkA5wMI+hcxxtoYY/WMsfGMsfEAXgDwXsZYXrG5dwUIQkE4CEE3SYmSUtCeHrhNTUiNHh35unDOASCT8bMUbmE15wAwfceqyDJXDAKpFD7wcOEOh545lyFT2T1PNpbCv02Z87CVWrivbrc7aL8WoE+t1KKDlXDOPnfs5Fgxia4nn4K3u8nYD7TkTU5jsi0SiMK5e1nkZdvXrkLL325Dz+uvBwaf7EDkU9z/+1F+/bOYrD2K9ORJ6kZe1DgvO0LNOusgtg0cew1O/twqXDLrw8ZtbG30FZNh1ssiR3MYVT4KXzzoi/jkvE8mHquvKLI5M0REb6897FoAQF23mfWQRGlkmcLflc8dNwW3ffxQHDLBHyIHQGvf0bhQ+Syc8yTRN3ldUZkTjDf6JKgzBQghgFNccELNkkRaLEYDB45tX2rcfvM/udG1e6nvyH03vzFYfMABYL29aL/vvtBh3BsCWnmcc2MgL8byzKzU2i0Jg8r/GYQxnLLhhXC1qDf0eOb8wDE1wTJSVAyWywWGldXlZ6zHjVM0AGDZmLFiOVIStVjMMXYVN3Qs6sGRvGlFmE5zzuUgkaC1t97JM/Ftz66E29wFzH4fiGRUPTNTznT647bp4aKu8szKwSVP8/Y3LQsdwJbaGdixLtRBEDXn/9hxXeQQjp/9unvlneGyBP0SaiUEPL0s6mZGxRzlM6375Cf4OUnBgIPGhbWt42uS2TdA+H7FCQ0OFF5He/6NAMlgiT7fxbNm8T8cG0xicvHvDS4lWRF3s2yUZ6PCkFSjw/al7rSvgnAAUD0snlY8mKOUsGv62js+qDlP6knd1jboASATeJ9z81VpGFcZlJXZhvKyoUQkcy5qzv0+592/GIdTll+KPb17YFt2UKpqwso1G7GyKWp3x0EcKyUFvg7v6UWR9O4Uueoz1jWzAulZfPw3jWFdmagNmzUEA0sOPDD4+72T3gsAYCJx4d+mf836KW455AoAUeFSueZcJNR0ETaLWOjMhYHnVNVrkfMw939Xz7fYTySUpQvPnF8448KCt91XkfQm/BpAFgAIIUcB+D6APwFoA5C3UJkx5gL4DICHASwH8HfG2DJCyLcJIe8d6Inv6yAgIMyKCMJRqX5844cuxuojjuROl4a05Jxv38NfRka9wmrOAczbuiy6kBCULliA0gMPREV34ZOnbVlgzEzjykkvvnDgd3TtwK1LbzXuq7mHG1hBzbn0c86777wgUhmes2X+2wBTn3OBk2Y1BqIzcRBtY0xwLIIHLudRwKQ2RwBw0xNr8KV/LEncplDkaA7dL/kCSpYVnKMcSHhtV3TQkyGUyS2RcaBeJHvDZGNKrMtzvWlXF3dKiioAy8F3d0VbzOlPq3CaBa29yC7CJbMuCZYPFo4cfSQun385vnoI7+AoxOhKs4XIZajYc0t+XQYBx7awaFJd8Hkwg/+iFUuScy4fL9PlomkzdyQimXPN0Bs/uw6wU6BeYSdM0uH94plzEfgxG4IlIzkd2CnNX8pg1/HrlxrGo9/url2BJsLeoLXrmSY7rY2Vhvc/LstTfMAB6gL/HRSGqm70CAq2R2kQTAyo7suWwt22HQdUm+oQpdZWthAjlM7J34fXximVFs0ClAVOrXweujFPJSNQZPYjzpjlKB7J/Qcbxm3qAZtfxjGVv5R27mJYnSSsJpc5aZd02+pWxIPPUV1eTWSN7fcqLrEk1V9iY8e3r8Py6dEe3Ylq7cv/jdL6aLBOTvYE/aUl53zGCD7+XHrURKQK6DstBPzEeL/rpz/F8ukz8rM68kBoq5QeVCBBMcicR9+73rfe4utMNskg18rLwVNm2ehMRzNj24ap51iIgJdAf2rOR01Tn7WJBzbgYzceBWBosr75RD11kDw15z3LuI3Ydne8rsJgIYmZYNkksC2HWgROh+7ghjXnJEh6jGs9AHPq58AhDkiCenpZtgobqw12d8xtCzLn2nNahnCOPG7NB5V13eNL4fmtxUzMEBOrUxZzJqWlqDrzDIWtdPWhV/Pv+s65GK4ZoaC2v0zbrdxiTtifcqcEgNt6r+8KfZt0TYENubQgm3D++9LZIm0NTUnE3kSSpWMzxkSo+jwAtzDG7mSMXQ1gciE7Z4w9wBibyhibxBj7P3/ZNYyxew3bLv6fyZqDD848khTWnLfu6sY9N/KHlcFC71JzlgmAUocY/O3RwAh7ecfLeG7bc7Hfp1oU665PHYY/fuSQQGBJV5e94dUb8I9V/zDuy/GdAZNTalJrv+bZa7C0yfzbOnLcWTDVnBvRl5pzLXP+yuzFymc7j5Bb0uRnWwRjaksxZ3RVpJ+7jh8+tBL/fHVwWoO91fRW+MFAPQcKH4jEZM08LxpNV/qcxxtkMrpfk4JKloMyw/XT9yDONetleb/hfAr8/YRFLHxs9scCp1wcx4upOR8qDKRXq44wcx5/X9qb1KYXLTt4ViTS1117F6qHlwJ1U/SgdSziaO0RpWwfHSv4ey+LocUV6E15/DFMe1WbCsQYWKjK9AAQEYTTLRPT+x9zXnaN5iwGmRr+jh21dYniGAsKu0s5rZ0hdNjht79qOe0kw4Gk90iMk5IBpt+XdK6TG6A+k0UVaVO37agYBwAo6Q5bSLIujcZs2UpgiFoIWxmJMZxRYMltQV/zwyr+ANCcSpmWHm23wPyjyLaxGHNG7KXEk/VHHLT87W/G7SkBVjavNK7D3AuMtrZM+2xxeVZaDqCu3skzSK9saC7IwRbvF/Wd8z2/upnv+7bb8343cb+iA0KB3T4CfZKkudc0Hg1y6ZfcD5vFvGuZUnX+T9IV0NGfmnMd1GMoKlE1JQYDQUAyT1IggmC+N895nY8+OpDT6hOSrq9lhc55Z8vQMEXiEHXO/X9hKQE6x3JgW3ZiazObptBRHI6LggIfp7j+5BZeWqJ3FUjHvDtra18HZQwvbONMK1PwycTqlEtPR/7g+7CKi5X5otjhTJ6gzlz7iR6JToGm4OWu7r63OTX/VHV+PaDiVABqaakJC4aHAcehsiv3JhKdc0KC0e04AI9J6979v3yIYcECGKe1C7pI05aQ4tFWNibuq9F9yX1a/RfyIw9/BJc9clnsd245+Dzl8/yxNTh6agOfSCkLDT4fv3/r9/j289827ktErExq7LLDLqJ2XbloPYsOYZDm8imfK7T25MdOru+8+7hL8OCJF2NUNc/YFadsRXFdhlBvhYHWLiCugWORvUprl2vwSXFIh5Sze0IBv9hW6ZJ2fT2a5CxuIBBDo9F0RRBODNLJjpAlOWiwU3AKMMgErT3jZeAyt6DeoYMBcRx5cvXQg8dGPRbpnzyYSMqg9LUnecZvO5L0vXSJ+XpGmiBotPbdT7wEVI0CqzSrLB9wJKehV5XyrKFMayeMgvnPCs3jeJSPkoIHntlBIOk0rDK1tY+gtQ9msCMW+VqpMYrsZk2VN7ZBs96CylE2H9G9B61FYUcJXa39qCn1SpszGcMPag0/SAZ4EFSTjRktoED8910EVSzGcNlRXEhUtIUrP+44MBB0lfFsqyULOOr1jsRWLDhKgIaSBvU8/GPaxMWnG8/CgWX38OXL71N2I7Dg5J8AX1odfJ69OFr+BYRBV5bHIzrvzpDZkzTuMALcs/Ye88qSGmP8Sg52v7ibl0LJDrBQa39tU2twHXaWRLP8wf4CWruapXebBtitxn+2WQHZewCJgVrBcDGxWWTByMGAQgLR7ICR27iKeau1R1neF1o79diAs7aijRYwuIypfu9MvHcxmfPmv/zVuHwowDPn5nU9HVmAAb1de7mtIKLjgBWwlSylXNC2bF5znuAyuXZWsS/GN8/B6cs+jVFtU4zbHzScawmktGBTnJXbWrILHmV4YssTfDuDPWxKoMnsVst/L03v8+wdfNxv26GyohiJ1pzLzvlCv/zu4MaDY848Hkb9JsICW/Ynx/wEpRa3z/JlzmfWhUKj/+vO+W0AniSE3AOgB8DTAEAImQxObd+PBIjMOQMDJYLKLmVIvMLrWL/06m38jwL7nANAm2TwqedlRSKpERE2DcKpNWWM3259Cen6R/z98N/3ZtObec9vhB9gpPnmHdmA74MgnEf4y3zDefPwswsOxKSG8lhae2ocd0hERkPPoD/4ry8F18CxLWWwG0q8sesNXPLQJdgkxJhlx0e6h2Kg1I0RK51G74jQAEzKnMuZte1f/zrfPs9gWHHiieEHYiObZERM4HQ/ndYeJ2oy2BCDdYqGTiW1GN6uXYHTPjXH+J2qs88e8HGT2Bp9NQZfe5iLcCXT2s3rorR2dbtlW6uwZWULmNQ/WH4PxJ/HTd/Bj6M75wGtPU+ARn51EnqlCpQdeSSK58wJnct+0NppJoM1xx6HzmeeLWh7U2azaHLYAYNRhszq1cr6uOseeYf8IEbdjDBQe/ZaSTzTv9AZl8KxLJx70OiIboOAlwuvRddzz0eOoRxbG+OFAJy4b699/Vh87VRO8U6P5YHjyhNPCEpiAGDK2rvC3emZV8tRM+dEuiZB5twz37/doYjgHimAXVw5CigP6culVWaHTwSa4jLnAnImNbHmXLtlyvxYUotcTzgPjWpisD211dDL3f7vkZ4j9VaI/TGkY+ZzUTYS0bsYIK09t3Vr3v3s/tnP0Luc/4akzHntB3mrJKqVOIz47ndRPHNmZPuBQB7zmGYHjN3MM8CupT6TIrPYtKUD3e3qdbzj0kPxwOdCsap+9TlPwpCImfWR1k4IF9SMqTmn7QXqDvQDuzd14LWHN4bHSigbWPb0NgDAyhd2DNn5xEEft+VWatm28Ho7lgPHSqa1U+Ip609a9RGMap+KOdsXRzdmBOOrxgMIO7sIyJ/W1qklrznJHjY751HbVK45Lz3Yd6ATGGhdLeq7Qk2ZcyqPbfyM/7DsD7H7jIOJhEZAMb5yApZevBTHjT0uSIbFJdcE5OvxP+2c+zT0LwL4A4AjWGipWQDMPZv2QwEBf6oZhIiXRC9JoKTrWOgrIDNaYJ9zABlpAlOcKNsGoxTPzfDVbevL8eslv07cV1Lm/Pdrv46iBj45mqJgM2q5wXfsmGMBAOdM4T1tL32Iv5XjdueZcFxp4u8DrZ2CwCIEVSUpvHfuSOV36Ohdyqnjv/nr47jnja3GDDrxs0YpOxTH++3T6/DCOjVa39YzeNHfRzb6QQ//tGWDmBWg1s4Yw/rOTcHnsOacRily0v6C2vs8FGIlO2JZcJMMkgO4oyuc85yXg0vdvTaImjJlDBQshnIGAJ1PPRW7rlAkqcDLBueJHzsgdjuBsTM5QyIpG98wtiL4e+rC4ais5xFoXS+iZWdUBOieG18P+0UDyjNBKUN5TRHSfllAtObcwsop78ezO7gKO6EeDnrtR5FjMFk5NiZzLoM4DpibA8vx43otLXm/oyO7YSNy27Zh1w9+UND2ERooA1IjJKVYSo3ikUZI79D4f/wjMNqrLviwcfMrXr+Dn7NHYVkiyGseI1vWlRmXB1kRyRGO9P7WnHNFoV2M47ajKLVXkfDaRzKvWs05taR3Tozb1ANGHogkbF4WHoNqOeo4AS47cM7zlS1JpWJ51NoPHRG2j1vdsho7unzHYf4HQUfNDdbd+BsPH3qUKpklwZ5ninMuP/d+DSVjRrEmQKo51wThkpS3+4K4/bBsFk03/QobLuRdNZIy5z1+zXnn448ry6vPPmtQzlGG7Ngtax+nrBPt/XTKsQjG3PGdl/G3a0PRxWyvi1l15Zg5sjJYxmnXg3e+g+qbO4XZfMbzsCxjIEYWgRv1kxv7vf84/P27L+P5u9eio5kzpQoJfuyNLpl54b/HR61/PzJrQ/vGIY5RrV2GTc3Oe8bpiSyzmCXVnIff6Waq9k57UWhfMjC8nblNOScdJlanvEywL0kfup6wPLR2Ier24vb8NeUlWjs0c+acYtm2Trzo29YupSAkfzJDtvH2FiNzKJF4hxhjLzDG7maMdUnLVjHGkhWo9oNHkxjxBeH4AygGKgCw3d64r8bD8xThnyRkJONDVmaERcA8LzAgcqUp/H7Z7xP3FWbOkxxpFrCnRpSFitg3HX8TAOCxzY/5+1CN2lzCz6GMqlT2fKO3RGt3YUWc8VxMVk8YF9sefRKX3/5G4ARbVaF4mCUMKssKrsN37l+O8295Qd2XQS2zvxCaAlQYe7JDUIhaO6XIstCIFs4587zIhM1M7Ik817v4gFnKZz3Dp5RO+HR2QWtf1bJq7zrnMcb4mJoS43IA8JqiAnd9Pq6TkOWWnPMpC4bHbidQUVeMkopkpoHswIhavo3L9uCRW1WhmtoRZscOUis02WFjHoNlk8DJI9IkS5gHSmxsHXV0sOzIZ7+Mqvb10f3Lj0gBfe6J4wA5N3AGe9+O7w0dh6Cna2+BY67BYaI90ncZLbhmV36HSmbPQuDBpsyK3Qfv5OruOT9zDoRlTTWf+5yyrV0SnmfNRRdJK6I15xFaO1Np7fL6oITAsSF73ArbIqfdu5YNQb9zIC5zTgHHIPw4Jayhlw14ncE0YZ65/ES8YywhqwUA9ZvbgzHJIQ7S40InT1C0AaAoVYwJVROCz+f++1yc8M8T+IeKRlhX3Kzs94hlauY8CKZK47XirPn3M65cAQBImr/n+jM7aMraMXXIvSt5rb0Q/Nv+DS4WZQqMWyXxauWDDd2xq+zYEK7zr+MBNep8JDPJMt3h773nxtfxhytVFk1/WqnpOOL9IX1Z7EsERweCEd/+NmouvABlixb1/cu2HbIfJPSuCDUV7JragZxeItqb+HNUSE2/fI/eKXhueK1yq8N7RwiBRSxerhoDm6aM67vSUaJx2isJRNVsiyDbfCiu3EawlqndZCplgVYCdNCwnMr0vOajtQdOeUw7QhNMtHaZTbShfUPkO8IHGFepBtJ6cqrd6RlZYRRgFv7wHN+v0F/JB5mF2RcxyH0V+0Ks6n8SBCGtvbabZ25XvRQK6oh+sX2BXHMeLIuhPPZKD2r7Qw+Gx7V45jztH55ZBD1uNLInw9Iy57NHVRm2okEUbF7DvGCpeGGOGXMM30ozSJIE4SijQIk0ceTNnIeRTs/PnMvY1Bxj2IgBy7+UgXMu1XhbvsGasoiifhk5Z2lwNLW16AvWtPJ+vQEJMk/mvNvlv89tbsaKufPg7twJOdAbZM49L9LL2OSQ5BOESw1XFXPlzPmftu3AE5u2hit9o1w4579Z+hu4zO1TXeBAYIoyE0Zw8rTZQ3rcJPE2PRKcz5CjHkvcnw5iETAK3PfzJdi6qlVZVzvS7JwzmdMrORdUZD58J9l6Icy26PoVAIKWWzqcUrk0owAH17G5k1NoptoE/5xzep14DEwCSt2vhPHozNp1yK4zBB4MiBikolVPHlsj50XV2p36BmUbOyVlRCQWS2CAyY6udj+SnPNAGd9RM+fyo8d62/xzquH9l1c/rGbOiZS9yPnj7gu/itDrAQCLr5QOLQUJtOdKfl+IXA/qZ847vGEYlkpuZXTmc8z/LTaKpk8P9y2VaTBCEku9WHkZHjkwPJeKXhgz5xHOJmMoz3YHzpKT0OKq9XbOoGC9aubc1J+4P4gTpdv8qU8pn0VfdNNckFmr95MfOlAtsC4/GmKssbTeIHEG+q6NfgcLaSf9UWsHgHOvDEWoUkXq8YaNq0D18JggaB+QGj4cjddco2jqFApi20Egpunmm9H6r3/5y/fOvCuCuYzlH/Nee2hj8gZ7ARW1kq6PrQU0CYGdILdlM3NmfXwLDxpl7ND+THtFge1DCJDZeSZm9dhI+Szb0akqf5/qfcqn2G+yTZ9aHdWp0O//ZXPi9auMtHYpcx6wigBc9zxvZXngMJ4QvPiAiyP7W7s7LF3Sf0/asQBCwZgVvONCfyUf5Pf9fz5zvh/9ByEEJBCEMzg+Md/b9qkrY9aAZ85tC9s6twWLnt/+PDzKcO29y7BZcj5XVY0K/mbZ0AjufPxx5FasREUPf/LbvPBFiYOjOef15YbaP8KkdhjRx0qINeh9DfNmzqXf0aeac5BIeX7c++008qxlr8N/l3CCSbHUy9nP8tkWMdL7g+NK61bujPbD7Q9MmXOZlqjT2rtfejmgpsu+lt3Lf1fPW2/B26PS8Y1Gcxz1KWZ5tzT7Tsi5qJWN0zQ3UuToJqV0r0U4TRl6Ahsfn/PxIT1uUuZcd7T13uMC0w9tRHltke+cF25Acufc/Kw6Mf1k2djDw7+l+0c9CssigeNqrZIFsxiItj8SU2qRLpeWF1BzTpwU10goNFNtQMEUdIE81OHMypVo+uUvE7cJEHlXRJY32cjKeWG2QIjE2WmVNVEzPXTUlE4VfuZcEerSngNBZ8/63QyU50SMBY4DeaayJNZWec4v+XAzRh0UagF3rOQOJra/wf9983ZFeDJAcXXwZ89BG8J9IGocC0xd/ffwvPzj37HnBhSR5Mzy5O1h5lzWDZDFNmFZ8a3UwMfbjDacyM55OF6H+0h7Lj6z5E7844FrkF3PAzvpAp5/2qsGzgcrcx5Ha/d2x7CFDGO+u4MnG2o+9MHIusGGnNEEoHh6YqyxmXpT8hnoTHnk+1dzPnxCSI2fPF8NVvPxd+/o08SBlJYEz8zun/wU27/6Nb68OGSMeS3Nxu8OBsQlTmImTD+UlwwV52GF7Q3Iz0BbJprxtiPNYUM4NGWktVf18qCqrNpu03Rg+wjHk8FCym+jNqN0eOQ7fJtk5GvzG0C8z/688ZkDPxO7qZHW7o/jvRoD+O+r+Lgs7C1ZQV1ATpLpp2sTAt5KzQpO0fVYXqV2/t39zvl+FAA5cy76EhaCjslmIZXcrl3IbtiA9ldewkl3hjTAyx65DLe/9TD+8NwGfOIvrwbLXenWWgbl1DNf4G9FGg5GlY+KrJchBCuE42lmh9PgRZOpPeUpLky3uYNnrUQvxE7fFtpeGz8hRgykfJlzyXjc0pZBR6963fVMusDwr/Je2EvruehTkDkvCg22bUcfCa+tLa9zLtPaa0v7pli7q3sX/vL2XyKGe1hzLtPapaxmxLGWhLzkbNYwzkJov+feyLFN1Le4uqQJd9+Fhi9eEVl+Slc46Fr6NOJTV2uKQ4E6CppIExtMmJzzFEmjMl1p2BpYffTi4O+iGdFeyIUizuEGopmag98zIbLNuBG5IANOPdon59wiJCKEFKyLyZxkmSQk6XnY8GYTfvOFp5Dt9fh3fEeX2OH9LanOAo5qWBEwDJvXhqoJCc5EAZlz2tWF3KZNfXewJfT1u9GsIkHdZZf27+D6O+SPQXaesSHr0WC8Es65pTnnJdXhvVWyamKcS6C17xo2HwCwZqJfHyw5zV4HD9gSreZcPQb/h1EYdVCUBJL87pmcXltiPNXKhlsC7Vty3ANae4LhLHDw6jBznl23LtyfnDm3iCK8pMOlLnK6c+6fzrrG0DmXnZ5//utKnLaBl0BlN/IMYbGXwwFN65AI7VmkHYMT8I2jtcfCMBc0fOHzAICSOWENftnhh0e2GwzosSxZ/I8EZQKFZc6DfUiGzGC0UrO0QCwvKxrQLgcMkkqpdoNYLrNQBllZX4a4xkk1/XOO4wKUB54wFuW1hrKXdwidvVGWSsqLP7+R7ZNj26YBqhgtYaLVMgKhYg8WJtXyeyFYILaiTslA8zBuTTXnezqjNgBJp1G6cCFG/+ynAIB/vrol1k+xLDtCa39wPWfjdmTN45FgBUyomoCJ9JPKOplRKmzp6Y0V+ObpM2FbBIR4ALOC6+NRasycM8aQkRhsso233znfj1gEfc4JAyWFi7jERVrXHOXXc+7aE1m3rYdP8EKMTBd/Sk+IGv0CNgXmDZuXeE56zbm+fwAAYUH9iKVQIflL8vhmXte9uoVnKx48iO/z1cn8390GHymSXcpXnyz39SUEz61Vr5UsAAMA2eNPwbCvfCWonSMMSHk5dL/8Mv9crNKMe958E5ZFYupkOH7w0IrwHJLPNoJvPvdN/ODlHwQBDIEgE6PQ2sPnRK/jly0ZeWwX2TVhHKoHKbx3c/G0aaj/eDTjXCod19Z/vMG4o4zutUFUPJM5K6SJsgRxF3en1NM5k4ndLu9xfSe4bnS0e4LuaE9ZMByz3rpFWTbqrm8FGXA3RwuitZ9/9SE47VNz+mVsbtsV1t4yz8Nzd61BtsdF265uWDYB7enyz116xmrKkaHR2v2KUb2oGJ1QMlNAzXnnY1yrIrtxU54tk9C362CitXc+9XT/jqw/96seBgBYTUsTv5fzKBz/+RCCcEp2HEA67QHBunBsDFomSve/84kn1PPy31UqmCzS+7/yd/dj9aSzQFKOotaOVHT89dq7OWV+9vuV5ariufThjdsQgVSHzlLJc+Upn5iNI5b/WGH6CJoyANACHHQgWuaiBLAtgmxCNxXKaKQc6+BV/Hy+fYEdjPubLjGL/snX+tw1TySep57hrjjh+MTtC0Vc5rzsMN6bvni2Wu5DDHNB9bnnYuzvf4fK007FxPvvw8gf/wijf/HzQTk/HToDSAka+QGf5U0rlG30uWX9myorQLZjklp9FQqijTNJzKW9BWI7xhIGeVnR1KmDesxNy0K7S5RTMWqukQYQ9IZPFdmxoo9DjbEV5haiOvTyChnTdi/ErB1Hxq6XIZJ3QBikpyDBnCjYp7bsorEUMoja/zJyBjvu1meiJViEEIz74x9QcQwvN/31k2vxzzlRAVeAl0PqmfMHNzyIZXuWxQbA/rXmX8Hfb21Svywnt0Qp7BdOmIoPHz4B5x08BsRpA2AF04ap5jy7cSNWH34E1p16KnqW8rlUcc7315zvRxwIiC8IR+FZhUeps0XxAlVxWNfGnTkRNYs4jwmzDvEoSpzkY4Zq7dT/1zRAMWzx6SqmOmJ9meOPIYtGcWPAdIaRzHleWruUgTGcw8wRqnPe9OkrUfeRD4d1oGD47Bt3YttXeGmBXIcIAKAUNiGRKL4MmbKTVJtuQrdfm7mre5eyXPiQskIyM7RSC1eaM+dWQs1adsOG6MIBWCvKnTrsc8ZtPObttZpzgRXDJEXRBOdcht5ruC8QWT1TPaMpC17bsgLlHWFttEVd7NzQju72LHIZLzBkklA3qhzj59Qbjznz8BH49M3Hxn53G50UfvC8wLgUNefNf/obADVzvj6z0LyzfJe3kJpzH25zslGSBJbj9y81tjADTKe1e1kLmeXLYzbOA9053+UL2m15NbqtBLnmfJJfB5ldvyGynS0yyPK7bYlaRq1eUTLc9Jpz2Yl4Y97nsHnM8SC2jd4izrapcjpBJHYEY0DXTj7etv3zTmDsQthpqQxCPrQ8jmx8hv+7UMqmpMPAVX1FGBwyZc4nzmtAidceZEt1UGZ4P6Tju/55pbWuGnLm3HMsZGn8O+8yF6727k71K826i/I/9rL45qE73sZ3XvhOMPZH4DManIYG/zwHLjAGmANQAII6fKs8f600IQRlixaBEIKiSZNQddppsEr6br/0B1mJ8dTSyI8ZUWu3HKX3+AM3vamsl53z/tacK9DlJfYJ59w2siRk5zw1cuSgHnPT8pAxItqpJTETxHLGgC0rWgAAZ39p/qCeUxKePu9p/OP0f0SWy6w+kShKyowDQFmO14p7eRJycm26YEhRWEA71+kRgSWbhsdrLtuW1zlPYnUmgQHoSbXHnCwBYcBpE0/Dh2eFAcfz7zs/b3kWAHg948GoA69nNACgSGITiilJsAdOnlsCK9UOp3xl0GbSVHO+6WMfh9fMn7PsBv6MyQHXvW1XDgXe/b9gH0VAay8wcz7t1Vcw8f77kCkPxdbSEydGtus5Y3Fk2a6uVgDAjnZe/xF5QSXjpOqss5RVlsfy0tr1zHmcwuKvn+IZfNOLUZGqUD6f9bxPLzS1uPIHrohxlqfuxJYMCmZwLPXJV1DQWzdwZ+iAPesxtVVSw9Qy572rVsEi/PqaBqXOjIuuTHivC67/8SEE3b785Jf58f3Znvq/RemtLLe50q6TfG6ORBm1nXj6WvfLL0d/UwEDbxwU5fa5F0TXE3uv1pwLPDf+bqysCfs19xbgdA1G5nz3pij9y2SsOF4Gh7z6/XAbRoPez607uxNr2KP7jy6btmhEdKH6reAv5rrI+QwCphmvhcx9hBiyUTPPBCb7ytcF1NwG5zKAWltx/0zZPwBo/89/kFmzBiybBWMsyl7S0wZ9QVyAy7JRfP6FkcVv1vExP+eG2YL3zOY1mV5ra2R7xxcQkh3n4Hfqv9cfM4rnzAmdc7nNWeQcLTT7rTDb3HKV1s6gOiNTT4aVLgraPimZc9sw7sx5X/h3KnToRleHjkIcrZ3ruZjviVc7JbpQ2va1yQR17QwlTarWiqwvkk3byHjRd76phzt6HvXitVIIwaSqMMC184fXY9eNP4mcv4w7Vt6B21febtxdkOH2vxPnVPcZcX3Oxf71zPoA5oLBQEm5yhrJpUN7ItXpJwV0WjuxsW11a+w+FVo7ZUr3jP5AD7ZaNgkCANleF0se3VyQMzOocGwzS0IO1PWhrVZBhzSUciUJwon3QQ5kjJhcPajnlITq4mqUpqIZe7l+PNBTytMRAgBv0YroNV/a+GS4b1iYXD0ZQOiYUtlhd3lw0GYWqu2tyJ67HOvqkgO6gKrMfvD4GmVd0bRp8efMWKAHlbO0wKRFYAGYVTcLlxxwibIqboy+fP7l+OCtL+KEG/hv9rrDMbGsKJxHROZcPIKtmdZg3bomztIzZc4VYUx/3f9CtlzGfud8iMANCAsMhTnnVlkZiiZNUhzr0T//WWQ7WhaNTO/p0nqhauN/xYknhOeltWKzPBq8YMNKVUETARG1Epl5MVDNGhVGr4kkemdyuH9+LKe7/fmUPyvLDx3F24PIp5zZ+R5+nISaw3yghpng5R0vo2zKdwCf2tyZcfHvJdvws7teAgBMbd2sGH26OuruH9/Aae2UGSOUs775MJo6w3thqv9JwopmTssTTvrCETwjKcbs5lt/F2wrT7gyrT1Hc4rKhifdF+IkC670vP6GumAABXPKwGKrx50/bD4OGn7QoGXO+9JbnhGKzlTITFh/1tl5xcYGkjlPqhEvpH6cMA8zDuPOWWdLJqK6nvhdU7ZeWjZySjVKytVnXO4VTXt7g5ZKfa135wcDundrjtmhnwIO/ij/u7vwbHhwj1J9Fw2iwjmPaUO59XOXY917TseKOXPR9ItfDkwZXkOEynnEF/i/ow9G5eevwJ7icAxdWT0m0GrISTXnNmOAZaHy5FBrZPiFR/B1EG3P5My5EIRTjy2CDhXHHQfLp6gb+5z7sGtr42vOq8aoxnbVaODqXUH9ueKcF0mMpYmL+b8Nko6DtCNqS/1z4xwZy1JqzmW0tCXXrB6yiuFXv/QiOixdUtkCTVnGmvNnt/L2Wx7zYruMzGuYpxiJzb/7Hfb8+tfKNvLven1UbWQZAFgVQqjPvx6hOlLcT8sL+Rixfc79+uQIFfodds5HTK7GaZ+eY1xX1e6CEDUb+buTfgfHchJLe9TMef9bqR194TSMnFId+T4hYeb82X+uwTP/WI2Nb/WfAdQfxNLaB3GM02EbxEaTBOHEEPNOswx0yM8TdQvLnAMAJRTUIAK9sSZsAzq3fi4mVvNArLgsnmQ12f5FoVYRSGkVSI06Hi0es9h4bNkulZ3gGSuWY+I9/4o9Zx5r9e18zTkvSZdhYuUEnD3l7IhtHyecWV1UjadXN2H1Lj8Iyggaq9KRcxTOuXg2fv4mV3v3MsOwZHMrNjR1IeNStY0n1PFJBJde2vFS7O97N2K/cz5ECGrOAbSW7MyzdQjF6SMWOsdMUjcw1JyOdA5V96FNpBWLF4cfLN05Z+ELFjM2itpHT8uc7+6QgwLhl/WXZHtbD0rJKCy9eClm1/NaNqF2e8L00wEAq0bJL59PZ0lQzM0HanD6fvbaz2A5nbCLOAexs9fFQ2/twLMj+aT/7IjZipCZqXWJTQgoY0pWPM6IdAeoBjOsdBhGlI0w0ySpOXO+rGkZaGeYpc1KAiJWHud8y2dUxU670iyWVgiUq689cy5z8dKOl7ChfcOAnfNnVjdh7rf+g6dWRduF6PjCQdwx0stM8inq0gEohScZh02bO3HBNQtxzpUHxX+feaiq659IjunYsoF01hfn4yT6L2V9tj2ktrFsDt2+amxPR67PzjkhQK5LM2bGLgTWcv0J3H5R9EsxCPqM9+NesIxvbOR5/gGg5bbbDI5J8u8effOv4o/t78uu8bMYw3zBT6cIdjqFdVVhpjhrO7D9ZzEr1ZzDc3m9uZQJF2NTkDlXas5FKzXt3fLPhdgWbN949Aw15wEsK3DeARZkxQEAjfPM84XIgvmnThkFZp8bri/z28FZDjDvIuDILwEIhRM9SEKXMQ44p1mG60orQ0c711vYnGEn6Jd4KcuYORdOt8eimfNsUOZv5Vfolq7biBoebJffy02XXhoKvwknWlB/4zLehUD6blwGPti/fpyBFmQPAsbPNve5B8CfCWmmPLjxYABA/Zio1ocA1TLn/aW1zzpqFM76YpSGLWfOezv5uOXl9q5CHLFtwHWjz+QQKtWZRFCTavotida+LyGTDtlaolsAYZaiW2OCzRxjh6atVavw0LTfAgDWtYZ14MKulgPjIjBb3zwXLR3lETvJy9TBBDkhZPfxnRV20bq6N6R92LAsG/MbDkRpqjQirBvRO/IhWEYCDBZsX1WOsqhzLs51fQdnhzKPj4uLf/QE/r1kW0StXela4V+b9W3R2vp3M/Y750MEMVEwQtFS2jfnfHM5N2BoTzd2LD5F3cBAzUwRdQISL3tvbUNk2+JZByifq9tcbGxay78X4wwLtXZdEG5nezhIFQ17ELC6MfuPs7GhfUOwnDGGRd97DId9/zF/X/z8t9YD5YsXw6mpQd3tv8dNp/FjZFsXQHT2ztd/PQnU4NKGBhA//45eF/cv3R7Up9uMqjOEIdNmW9w5lweYjN7mxUdfae3R77tIWSnM3hDdj5I5lxzwuuI6RZ11REv4neKiZLEVUcNTuuhQlBx44IBUXJWrb6lO0e7u0JEeqCDcKxv5Ob+yIX87GEEj8yzVwdPVbPVgy0Bo7flQO7IMjROqYtcTRrHqpcLHDxkmY1OPhXS98KLyeR0OC/7e+oUvRPZXSC1qcCzCUDczoVWj1obFhNqLLxY7C7+2O38gRoZgPsTR2hVYltFxcUaaywGGX3O1GvzUYFdWwhk2DI3f/CZfMOk4YMxC4LDPwbIIMtK7kbNSsP0xmNec+/XguRyI4yjnL8ThhHPe9eyz4U8Qdb+yQUOktk6WjcYuztJpaPLrcA3GOoMF6htjFphac05dbHm2JvIdAdHr22MeUC4xssQDSCzgzJuA464GAJz3jYNx/IdnKsZeXNDTGT5MEYR77+XzjNvNfusWLHjl+8Z1jbfcH3/uKdtYcy4MU49GM+dvjSdY08jnt7yML+l3iVdUrm+Vs/gicx6UOHXE1IUWAMVBi3HyxbOvBwGGUtG7L4gLEBIANb4+QtL2cnaWShRg3kptcM4xOCcSHo9pGcK9hhTPnLdpHVrimBODAd05X/fGbmS6XVgx7TvFNRF2ZU3jOyMKp0Mef4RzbjG7ICZskRf9DcPLhwVZ6e6s1NlGUmsXcDTXTHeKH1nag3uXbIMOmdY+qqZv+g85O4M/HvQNPDPhzmDZ1YdezecRQevXXpLmXrPd9cauN9QFjATmtCzFFHR4irwX6ueIWrscpPfXlTr7xnMzWNjvnA8RLGLxPud91Ox2KcPKmlC4iNga7dRQH6QbMXet+TtIejeW/N8tmPrKK8q66jPOiHy/6M5HAKj1HjKCVg8JNeep6tdw4pyo8RvnoKZzYU137ZwFyKVEqiUF+NShuKhcQUicBPk6UXMuKPA2o1rmPJppI4TAoyrDYfrVD+Hu17dEtr3txYEoTPPfr9TRyGr0cis1yeiyLAs0Ezo9h6wKzzOVLlBMyKNGhkYhCMoCPvl8uFCbWLZ3bQ9XDdAiCozWAl6z0RVckCQi0Kg7Y7rh6nkDy1j5OPFjB+TfSANhFL0bN+ff0PRdwysQyaYnZFCy69ahomNj+F24KJk5FSV1oeOSbkhw1i0oImEj/u//+B+1US2NWPgOadfTEu24j/XnuS38+hXinBNCjNThyhNPMmzNOxck7i+VwpSnngwp6WV1wEf/AwyfyVk40k0a3bkLU1v5OOJKfc5ZzuWZcun9z+5sAwD8+vwDgBX3o+OR/0aPLd9rxoJsLLEtVLk8s1HavYOvNrxA3V00yJwTwlQmkefByxiClz7bxvNXRcZw8Vl7OGsayzBtYSMvy/GxqcM8fo745jfV8qOYsb6haQkqO83vTtnrq43LAS4I9/aetyPLheCQTmtfOo7AokBVaa2fOU8ejGRKMfEt1bjf0Hq73yvev/dNN8WzNOJAu7rQu3KVmjmPc8785WK8K5k7FwCQLlRMcYgxenpMQIgAxxiovk5ao+HKDrlM8+9nn/MkyK3UxKH2tk5V71vL0PXMM9GApkljYpDgpNUf+eDNXE07rq2oTGu3bIIJ86JJpXcCY1vDtsahc25hTxkXbaud3DdGG2UUzNcv0ZX9AV8Qzsf2nKpRo9tJuebDcfMTayP7EHbpFSdMxaVH9WGe9Z/PnnSHkvU/ecLJfGzyxwO9y8VFD5jZbyuaVymfq0uKAiaUbDuLv3W3hvricQJ6zbkCwTIoSWDWvAux3zkfInCSlRW8jIXCowy/mHsOGq+/HiUHHKCq8AJGp6mmTBZYoLjhte+jbPxNIEXFikga/37UoPKIcLq9oO5ZPaRfi+LPNCF1RquT03adazswtu569B6Jmmk5YdSLpcD8qKM3gAlkWFc0ohdOxvz3iFplTyhjMgpLynroxjwpKYFtAU2dmYhP84U7lkSOt7szg1c2NKO9t3+0aI95SsS08uSTpZWesp3Apv/+G+720PmVkUoV6JxTauxdXAju3rod3288Tq0zt+PpxAN2zvtgTwnhQ1entWuOt7FGbwB15wLDxvW9TICAwSnq370wZZki2XRDoC0ndW/oqBgXfnf3Mn5t/PFi6osvYMIX41vHkPIG1RgVY9ec9xu3NyGzJt6JKhR7fnsr/8PQCizartFCbseOyHZxfc4VYZo+wiIkrOkmBMN6WoN1WTesOWe5HJBOKc5x8QROhz944y3A7VFhOb5P9d3qWeKPUbYTjNVMjC+GsXbV0nZUtXOq4IENW1XnXNq+4fOXB3+P+8ufMfzrX0fOEQFdPdAlxkLziytnrH/62k+N21gVFUrN+WA7PDRlft/EWKX3OfcsoNIqwdiaCbBggRno+DLzQu660fA2H6v39AxdLfLmz3wG6884Q2vFGUNr9wMHmeXLsf3aa5HdtAnFB/Q9qDhUWPhe1eGY++Yvg79LDJkzPVAiaof5Omk5Sy5B6g+ITSKZ85fu28vUW38u09lfxu4sg4S4QJMTlzkX9mWOgnoMr/9nYEmNgeL9Vx0cWRbUnMNCR1Ezbl50ORrnmjO1zfXmgOCu7l3B2EAMpVKONL4+1bVBWRdN8tl4e3uURfPUah6EOXV2IyqKCtNncT2KdU1dyLWr7/kNi29AWaoMsO2gw0ShomsXjlb1smwS7sNEa9cz57nWQ9TvJ7yb4nnT2QXvdux3zocQ/cmce5Qh46RR/Z7T+D40arVJ6Gx4ZRjBW7ebZ1SI3WOuoTJk3l1JWdLknIvax1U7eDQveLmI6nRSyUn0ekegd9v7jX0XJ23j32+/775gmRBB4z0OfcNuADXnruXgwctVx0FE7kRtz92v8wiooLVbjKoZGc2YZz09+MsLfOJYtq0t7zkcNaUB5978PC77U36VTRM86imRStESClAdSDk7Vf3Vn6Llb4ZewgBSqcIivYz3lenr6QKpMkzKuTht9iWqQ54woA9Wn/NCnHQRrS7tzUNrz0UN14FS28fNqosYimNnRmmYJpQPC536dHHh18tYc16AAfr8wmuNyy3aC3g0uNZ2WYlOilCP1b0LlhP+5twm32gxqXfHoPaDH+J/SEJwrI8tCotn8gyI3loMQIQl4e7caQzExBmc6fHj+3QuMixLGs8lw2zmnvW85lxkzl0XxEkpqsp2la9W/ToX2CyZbGiHpL3DIb3fCoydzrJRWDrzo/By0bHWcxGouleX9irjIZOc7vSkUBclPW4caj/4ARw47EC+D30Mj8mcC5iE2HQQ21ZqzgeDKjzqxhuCv2tbzI5ryNKhQUs2AJhQOQ5TKieBOA7vT2x4PounhL2kTQKUv1/2e74uhso/kPGn+0WuAaOIgMUJwklOe+vtd8BraTEG9N8pOFrgpK7ZZzgQoNjmweePzf5YsF4fc+XMud7nPEqtHRgICWvOxePatDmhzGcI0btCtet2fOvbQ3YsGsMcKSo1Txbi/c35nW7eaXkD03kKfQKL2oHYW1xioceLMrsWncXHSJGssyW7rtJvj1pfEZ88ycci3d2RwaY93bjtpc3+9gxVpSnUl6cxd3R82RwANHfxecHrUllg4yr9wLxFYmntAkeNPkr5/N1/b1Q+W8RGjmZRNPwetGRCFsdaXzAuyfkGAEdKSrp71EDmls98lm+z3znfj0LAH2KCvrbhET39xIClU6ttg1qxKxlKx9/4RLitYZTLZ8iYjIN1u/kLdO2/3w7O0YTne6QBnzkACLozUSPgw9UnR5YJpKqWAH67iqB278trgS/1LYOWs22Mq9MimyKmoAVMPN+ItfVAQoJRIto8JOH/HuBtul7f3JJnSzNyLKcMOCwrGXUJrdTi4CRksBVQ2r/2KsIJt2y1ztyKP+5gOecA0NKVxdbWeJ0C5vcMLc1qzmGE1h6dCGk/M+ejp9dg/Ow6HPG+KZF3q2FcRcy3VEwaFR471Ycsuuld1wN25ccdF9nGTZlFlCy3iztlYhfUTaZHWlD6oXtCbK4PznlqBFeqt0uld7mPjBrhQHe//HJknVG1uA9iSSbRyEJhE4KU/6zJLJ33rH+O15zbknOuCcLpAa+6Uw6MHkC71z1vvRV8V3x9+4hF2D1sPnZuijoNFCSgtVsWUanzBrVcGadOOBUAFJo6/57/LMc554W017NtNe1p2NXM5X/Ivx95l9XVwd+pGDtYrjmXM+cjShqRZjaIbcMmNjaOi9Z6epLQIrIJvzHm2RtQWY2/TzVznkxrl9H75pv9P/YgI77mnC9fevFSXD4/ZHLojuLWla3B30xzzged1i5lzneuzx/MH0oMdru0JNAYtqQeWBEQ/p743mHnTB6S8xoIxHPk0DRcX7NGPC0T5tbjwz88ItjW5L/OPIIHT0Wy7puHfjNYV12axtNfOQYzRsZreGxo25B4fod897846vrHg881pXyOnTmyKq/NnwvYHer9EeNdbuMmdD7+eOR7Mi6YHm2XK8MiFpp6dyJd+zzu2vSTYPnV9ywDEJaYhlCfIZnWvvrwI2DCsWOPTTyHdxv2O+dDBN5KLcyct8xYE6xzEgQlXMpUp1p3EA0O47butSC2X6Mi7bvQucbL8xTogmdiTktVvR77HeY72Dc/Ga2LWTzqqMgy5bv+Y5kTRnhZvSoqVAA8YqM0rbeJEi881bYNM+eypZfUeoz2QVq0t0CF1tMncuX6903lPYA96inOq5I5l4wokZ0y9f7907GSyEiBkcV+Z84zvgFKLNUBSzjuYLRSAzg7+5Dv/heH+8KDJmxu4Y57xlYdkQit3eCwsSSDOgGptI3TPj0X1cNLI4aiW+BzkSLh+Zja1MShdVc0gq9f7vSE8QXvz7IAeF7oV9EcEMtu4eq88lBWfvTR/I9Cg0RAMN55baFxq9+v3lWr0P7gg7G7kN+b6DrDfSUEVllYDuRUpuPTOQMwei1CcMR2Xo9JLAtkJC+7YCCgLAyumgThUsPV+rqymVHBOp0p0PInnmUntoXc2jXquj+rLS4BoGFcZdD1wtLLqeQAieEaBMrmRlp7/MSU9fIHwXjmPLnmvHFnNBCTCFlsLyYoSxlFd64bLnPhyk4ipfyZdGxYxEJrVfT7Pa+Hc2VS60Z97Ano8IOgeaEwgvKpte+jMDnn2953OH+kDFOyHreWx0Q9cz7ognBW6JxnuoeudVkSqt//ftgN9Sg7wuzQDAXiNBfi5i4RFKGirnuQgyR9hcm0E+KBFnMkQVl+nkWlDoqk0lIX0Xstfruo5x5Rqo7XY2pLOevGgLEza/Gfjf/p0zkPr+RZ+LRNkPWva1NnBtOvfhCvb1KTRa5gk+jOuZTdz1e+NaM2bI159aFXR9a7Vmvwd49nCARHnpk8gnAGLBqxKO827ybsd86HCMT/n3AIm2aHAjMnFkfFewQoY8qDqBsK1PCQPtt0J8om/YhvL4k5FDrIyeUvlXJPWh9nHjhK+Swy58Uj7k7YqS/kFImImdWWRT0wdcuCzHnG7Z9DBABja6IUoSBzqbW5EAaozTzlWiRlxeLYAzIuOGRMAWcaosSv9S2yOf1cF4STHUSZfiic8zJD0jgj+UGpmAx24zevURfQ/gvCAQCIDchChgnO2GBSkXJ5+soff8OTAIANDarYim4Qm8SSWHbgiu26IE6kzmrXLpjQUBEee9j4wuvWlz8b1R6IGB596CjQlatUas5BPYBSjCiJBuBgARh1EPCeG8Nji/e5D7xF0WddgeZArH/vGdj6hStinZ6kwIrpO8zzUHn6ezDpEW4QFTWWx5/zACx6ZXwmBKkiHtCyfY9CZAs6HnoI2XXrFAcyPapRPY0SA9shLnBg2RGatNccZfcUlRcHmXPbJmFwBZqomSGIKYunqQfKGa/Zfevuw6K/LSqsQ4dtqzXnBKioLVBPIwbyWB/nnL+681Us/NtC3LX6LkUQjlEPzHNBbAcWrPgWcGJ7140vh9ADg0KgbRBaX8UFd9Vz6/+cuzdgEhWreWl1nG+uiKUCIXUaUJ3Ioag554JwfWNODjZIKgVvd1MQpRBdHipP5cyWYV/+8qAfU2TA60arY1KsIJwQHPa/N9j3oa8wBRfEfbRY+H7LjEV5Lpe75wiI3y78AWNux7LxRtd7sZUtVBZversZZ00+q28/wkfKtgIF9+fX7kFvjuK3z6i6B4HdxNT7U2h9OQDUlYSt3d4/Laop047Q/9ncvTzSXUe3p6mrsgoTBeF89OV83w3Y75wPEUTmXBiynlTXXaSxOitPPz342/VU53xNs2qsMClyPKFsXng8Wxhb4YBRaJ/DrOQfmdoRlGp0JP4i5Zl0mC1tq2L3z38RWXb4yMP5uTQdDxE1yw4giu8YnMvAaJIDGETOnDMUSTWPes15xQnHB3/nS5ynbJK4zV2r78LsP85W+ukGapa+QasLwsmOxK7v/wCbPvIRvp2fnSoxJJ0m7A6fgThHmKTVWnRGPXN9bj7U+rWnhKiZ84R97c2ac4GmSvXGsF6tpZdsoIrMrVbn1B9UDyvF8R+eibnH86AN0bJATb8yKzETqd7s+ItnGrcpFD3t6kMiR8SLnahhns6GdNwtnROBncs1WruL94z5TfScCeP3fcFHgmVGCnkemOrL42rOvU5zPad4b5xhUfaNawqIuNzRSo8Zg7Gnehj1gbmwKyvRcMUVSE+epGw6UEPyDzP8VpmEBPoitv8+69kCxWnUA23VBjXtmHMjhrGxMx1VumUSrZ3YFpwRYUCgd3PIZCg7dGHkuzIFXAF1jS/r157+GjpzndjSGe16ETl/y1Jqzu2UhWHjCysRSdqnQPMIc1mHqAt/dNOjSp/znldeRebt5SCOzVup5XGkWS4XWzIVK07p/ysHSPoM6f2LE4SLq0XfV2DKnO9+39FwcxTNWw0ZOS1g62XDe/PI70KHYSB9zuMgZ87fKWQ3cY2cHdd+CwB/9pjrIjV2DGBZqPvoR5K+3i+I5193xmMz5/5l9/aRzHlxWTTYGNScMyvIfsvOObFIMC+aepwHvdyJKhAog8HGsx0fxr92fhU3H38zLBqOEf1tK9zSncXqXZ14e1t8C0Y3GK/U6x5kzg2ltDKGlarz6trd+XUVPvEXVYdJMFHnD5sPt2sCQNXSoEIy54KFWeL0rYXcvor9zvkQgddAhZlz+UV2W1uDv0sXHYpR1/8w+OxRqjyIHonPnM+pitaLyrT2fA80TfOXjkqbedSNDBy6o8tfpMImHVPbNXNPXRG9IzhoLI/C5eIMiAKQSmw5Fx6/vrxI6XNenpMGQS1zTqVs07fvi7bakWERgmyCcNU3n+M1R5va+eR57XPX4p+r/smP4z8rLnVVapFGz+16jrcrE858qSG5K9PGY7PUloWyo49C8axZ/oFY/0SAhENOXdV5SPCc34lopxYgxq4f/Vhd7xuxTkMDqt93LgBg21e/NijHnrawEUW+AIxuhOQ2hiq153zlIBx3/ngAAJEcnL7Q2k2o1nrI7rnlluBvZqAby845ADBGQCz/PfI4rT3tRN9TQsAZFDJk53z8kcC4w/Oerx4g48ft27ggHBwTZXfPrb8zbi8c5bJGD3YpD17VX/rxQFwuwABrOYUgHO3sDNgmQeZcd0QSas6JqbwgZog2taJbMfF90e08BuYfR39NqcSIIgbjTQTdcswP+JT7jj01Z84Fntj8hPL5K099BU09TdrOHcU5TxXZyhBz5NnjY/cfB08KUrU3mp3z4PDEjvQ5F+dlESu/kCnT6pvlll76M+q/MyJzPpAMuhIci+1zHl1ecXK8Rszehsk5J8X8/dz0drRDi6zODgA5SfiwdWf4HjBv8GvOd21oR09HDq5BbHFvIbMiKvDb+s9/+u1Sh2buDQISuh2ZkDknJKS1v9OZ8+LyFKxiLYDvMTDG/A5MmnPuDz5iPpeZM6vrX8Hfj7wu3I8QJTaMzcu2Tgn+rkxXoqo3DJgKZmlf8cI6/k78+YWNsdu4eTLnVaeegtSo+OPrpYmFCCvq21T7NfKU0Qi9vtB9pqwUPj//87j9tNvzbvtuwH7nfIgQ0Nr9SNnTW57GcRfPwPlXHwIrHWYV6z/xSeV7HmMqhUMzTql0x1xmoKBJUTv5ee51e7G9U6W50mJuVAlbuyjLUHf8pUFGNg4eZRFquA6nfHW4rQYjlVSyJNP+pGHKnGdcD3s64ynGVhVXpkxkZfvnftqcEWCkByzdyr9DPSyrnRDuq0jLKGdzuPo9hWUuMy5FU2f++smOLKdY37n6zmCZ4pwrgnDm/QkRpeqe6I/uLs7vnBPbArEdJUvTrwlS0Ne9bMF1xQPNnP/1RT7pFCoBkGubFwkW5HaqrbPEdRh25ZWwq6r5Nob2Wv2FoMjp17h4Zli31TixClMW8Ih0bE/iPDj83KiwjikrIGC8hnrRJpMunxCEMwVYLESWO/VSdpaQwoTdDJNyrIOSb7nJITH8aNlRBvOUIEOEUTJA5/yo6cPDfduipSM/T90gUUSd9PfLJKQWcz16l3ODvTymj7gAowykspofTvudnSujjpCMSOb8vL/wf/PUnOvilg+ufxA/e01ty0NsS7lvTsrCnMWh8ThuWjSL/rWLbWxKaINLpDKcfG0k9T7n4T54zXlBAp3SMYa3houza9USET1z3tfAlLIvWRCuALV2gdQoQyeAdwhy3/JFL/ByrKQ5xNP0cuTMuQzKBj9zvnsTn9tbtndjxORkxeyhgqmEkHZ2cnbcEInEiSzzro1q+VhHS69pcwDcQfdEu7IY0b+9icoaNYhNPRYEHYRauyg/FAj6tWsi0J4tvXcic26wizc0hWMYIQQ5m9t7o6fXxIq6jf/q/Xh4WX7bhFIWTqXaoR98a7u/WH0egsSQ4ySy3vQ2kCbG7gFFH1S38d+1s+fz33zoRJ6Q85iHmSOqkdYM+CdXRZ9jEz46+6OYWD0x/4bvAux3zocIuiAcA8P0RSNQN6ocw78WZuHsmmrle55Gr7Js3TkP1xkz4zGZ86ueuQon3nmiQjP0KrnoUa0/horMa/fzLyT+Np45LyyCb3LOK085JbJM7kGesfhg8cSWaG3+ZX9+FQd9J75mv+wQ3h/RdG2WN3P1dEIoRlWXoMixkBn+I5RPuR4esWAzGtSfA0DNBRfg4bFhv0WWyeCwSXWR/cr45ukzseH7vA3eUwUMKG2ZqIqrMO485sGWoplxtbN/fpuLOdX2Rp3vnlJJRCqm5rzruee5YyCyNIwmtj+Lhcice2bqqkBtcdhCbKCZ853t/KEtVKCvd9v5kWX6xCeEk4jjhLWogyiUJCZm3TZyRqgiMQGNuY/q5AI1I8oiy5KUW7Ne+PzsGHYwnjv020qW04LHfSLZOdecVwCYtvZ2TmvPqRna0oOl/rHERryYnLRZOqrs7u7cadw21uEQWUeDgVF22GHm48rXXmkLqN60gbbxOmxyQ/jBz9YfsnMFpjdviNbZyXNBTjN0Dc8Ii3lunPo6kFQKVp72PJ5H0etwjQPikMR3WkcgCCfusXjPvVzifkyCcBHRSK3m3LItVHt+eQKjSO2J9hleO5JgrJ6Ar62FJboASNf62Z3Px56fQJnBzyCO0wfnXPpN0tC18QOqEUv9Ug3xDAu2VH/ANFp7x+OPY/n0Gcisk2pQDW0k8wUr9iZsx8L5XzsQRz/1BZT0cqfAAkHtyDJMOrAhsr1OazeJcDLG1KDjIMOyCUZOrgYwdMeIQ+miQyPLGGNDmjlXHEEJTjre3aCUYe1r/B0ebGG+/kC3SyhlQVD9k/M+id+f9HsceuRMTJrfgEPfy51BIZop09oJs3BAHe8fXldcBwQ151F7xfXCY7atz6HI5WPT1EOG45gxx8Se67/8lsBJ6M552OZ3snG1oO0vH+cBQaIJVYsAK3FSic653mHDxJZtSKuJAhF4diyCxspQL2Rp01JkSRNOmDkcJpiu20Dame7L2Adeg/9N0BxDRdbcx1imAepiOh7VMudaBOmfb8gvYnRwJYrTHP79343coc3S0PihVdx4P+/pvlHleAAh+TtepiHYVkd6Eh/MZAVR8YIzZqM+xes6J1RMjXz3iZXJDq/IPnZVJjjRVgaOTdDWnQNzeAaIEgKbUViSw2BVVuIn80NxC5bNIhWTkh9RxQeYQoQrZLRrtGFAzZynFLV2s3N+SCMPINT0Gp4HyQiMy5x77e2AY0tZGlq41L+M464BiquBhmmJm9175r04uJE7aoNVc56OoczFYdqq23Dwy98FAJQdpXUPEO2tUg7KDuMKoOXHxE+OfQWLyZw7NTUAgOrzzuPrfeOpv5nzAitPjHh75iXoLa5DZ/noYNlHZ38fYETLnKuBnLlLfo5Rm5+Gl7OAlg3xB7DsgoIORRMmRJbt+c1vzRvHZRUFrd2QSZZV2RU4wjl3ld9XPHuWtoOBTaFy/XfRpNCAufGpX0Sy1UqteLemgWDqDx5X70osgBBYknDRiO3PRTZ77s412FDPSw/sPgpEiqxL0J9XjD00OXPODA+tHgCxDAGbDWeehXlv/BSHP/91bLn8cgBc6HL4NVdjpFQ2JqPipBOjZQqAIgoahzbTY+PwVmpxzrk838njMs1zaVk2qwQH43qh54MS3HU9tD/AOxz0Lg1bpXHBR+0COPuOcw4AtWOrUX/+OdixmDs9j1grQGLE1/TMearIUO4WMx4PFiw7PLf6MRV4+f71ePHf64bkWDrGSmVLArS7G2D9bJdaABiN6qkAvHNJIYhTLd+b0AME3e2ZINCTTqWwoHEBUkU2Tr50NsqqeQY9FH2jePvARwAACxsX4sdH87K5+866DzTInEeP6XWGopzlrBJHr+WJhJ3r2zGnYQ5OKI521ACAF9bl18Mpcix89wHOmHpmdZNxG0bVcVUEKEiezLljOXhjc2v4O6T38MOHjwcA9GTVd1PcYo9GE2kb2jcEvdd1tN2lilBXnHC8MYD/v4B3/i34H8UrD24AAEzcMzeyzqoMFZf1msqenFpzbmnRzaYeiSJjsgGk6BeTswv+aONK2RJarlJ38vjbATzK4NjJBoLXyZ3k/7wdzXKJnrIjvhPW4gwv5ZEylqtGicMtn5TVdwXeuksvxY/PuQq7Rsf3yiwZ+Q/YhGB7W5j+8IgFi3lBvScQMie/v+Ai/jmbidBtBHr9ujKTEF0STM65LAiXkl7ROOe8vqQeFakKVEVLSQtyzkNau/9sUNq/bMnEo4GvbgSKkxXFq4qq0NzDgyKyIN5AUJY2/7Y4jNr2DCq6eKBLdwCDici2UTx9OgCgdMFBAz9JH8JH1J0O4pdR1JznB4T8rH2seFMeFGrET139d0xY/+/Y9Q27XsMl5DykrYyfORdq7SJzHj5jPSW+OAwlQA83NkrmzYvWrRaYOQdCB7poCn+ndYaBQBzdnSXR2mOubUBz9nJKK8CaCy7AsCuvlE5ugFOof+1KFy7EsC99UVkV1Jw7Duouu0zNdEVo7Ybf4f9euYc3AFilJWDZrJI5T/vlNVQqnenpkMQxbcvIEhj7u1uNP0uMNRfcfwFWt6wOAxzN64A84kYyswYALIOZkk1Fqeu1ratQlG3n6tTgQfDaCy9ElSS4KoM4KWMwgBbgo+0xDHGsN5NYc64YkX14bjLrN6jnl6etkYzOp58Jz6+APufM8yLGroka/U6CEILGa67BgxdMwIeusLGkaFesMjrVdV8MQ2JQOjzIznnjRE5lZywsZWKM4aV/r8cr928Y1GPFwaQHQTu7uKjmUNHaY8T16scUJtoYuWfvAPTzf+xPK0JRuJjnRG6Xtr6HZ6OHVdejPM01LPi/oubcUO4pLapIV2JYFxf5XPOqzwqKeTxbuuM7LHz8SG7bHDy+BmfM4+UpR02NMkwAwOsZDUbDuS6gtdsWqNTKVMe4inH456shW0k457+8cD52d3D77rEV6hhiE4LHVuzEna9tMT6GJVog56QDuH+w/etfVze07Nix7N2O/c75EEEYNmkaOpjihZRrmeUWLltbe/DvJduwpSU0XnTn3JPF4kzGt1JzTqVt+QP88o6w/2tZdfiSjt/BYBc4JlIG5POFmEHUIYDv/FgloariJ+d+Eh+b8h143ZNR5Efqe9z4GiV9cFu3uxN/fG4DiG1jY93YSDTO1YxX2yJ4e3voGFMnh5KapxXnPOcb9U+OPhCVp54ClnORcswjZHfWd85jBu5cHyYc8dtc6iKF/DXnq1pWoSPXgcqu6POgCMKRmJtm2T6tXcowDnH0em0bn7yW71k+KPtzB6CKqzt1IlNNnFSgVBqXvV51+BFYPn2GcV388cyZmuA8/GtPLJ7h7DelvoBLUnbYYZhS34oJGx+K3YaAglECuL0q/dPLAaseAna8KW0dPej422/D6J/cqO3UiokuGuCPgVYlN3Y7H33UvF1cdD8hcx6XESBK5lxiOhGC4hkzlM8Dgh/MI7YV6ZoQ1JxTzmRR1NodLXBpqDln/vVNjVWV3Gs+8AF+TFmIzD9W71vLzKcZx0yJaTcpZ4//s/E/5uCBhMp06O029zYrXUOM17iA656VBBYB4PJLNRG9uFaZefZtESu4XjJ6li5NzJzL949YFirf8x6+PwocN9Yg7upj/RlnKJ87Hokv69LRuyIcX3uXh38zzw29Afm3uLmIc972zzuxL4KCobeIwCIWV0Y3tNL0dEG4bHQsFSVRg11zPs/vysEoC87tnVZvBwDa0w0MYc0586LO+Ye+e1jB7Q63r413BPcWTIGaf//sjdh1QDhGUsKwuXo5Dj93Mo44d4qyzYw6f+4wPAYldqhyLosVZrr52JkxlGSY8NPz5wV/f+xIzlL1KDCtkQdHtrbGBEdpKTpXfif8PX5AteVPPGMfp7vDwNDUEdqmwjm3rXAOO+kANaBuEYKP/OEVvp2/jVziWaJ1iIoTtya2Paglh/sS9jvnQwV/wJeVG00RddlAWGdqQRBxzsO/oy1bKJRacBI93vq2sL7MOjJsgXPlA+mCnXOPMkwdHm25piDBORcGsfzbU3YK06v4+RT7VP+b3vountrylHEfHVr/9LN/9Ry+ee8yuB6FR1nkZe7KqdkGfb1HuJFkS9fUlSZ7kkqB5XKxtPaMT5+LW79Eov0AIVNgbkOUWSGeE5e6cFhy5jy7ZSte2vESAKCi23ADpQlYNnInPfRg8HfZEYertHY6dJS3wcYhE3iWzcujYpzYl15bJ3r9EseWqOXm6HR/WqyJ4EvEvqcGY9m2+01rF78qUQSOUsBxYFXEZzUIY9yP3rNaqzmPnlddc3IXgwCWHS/gph9fOOdSULP7tdci28VmA8VxDI547LW1LP/8mJI5BwCSlq7nAOs2Q4ctanwEQwmlXIhOficd1ZEH9TD2D7/H2D/9UVrGn4Ca96tK7OF1lK+/UDOKdyy733gjujzGwY041Jp4kg4Tgyg4M0MRalmXKm5qUhNuuV1V7d1eR7B5XmgkZjdvwvArr0TR1KkomTVL/3osylLmUgirKA1CCCijmPSwIdilqO1bKF+8mP/JzLZBHHrfeqvgbSEFhXffcEO4XH7upXvFXC9g8ASIC2K8w5hTPwcAcMqEUwrOnBtrzkWwdJALwoUTRz0mZc4H9RD9Auvu5pnzoaw512jt+Rxz2eHN9b7zzpYpUCME7uKCOO27udNL/Dly3vFjkS5R353vHfU9AObM+cT0s8Hfz98dCkPOPY4Hee5f6gu3ecnX8ox54Vgo7FzZPnpzS2HBD33cdXeb6fCUURSlLDCvyP8snHMLGX+cmdGoCiLK13DDHh6IkDt1FGnBYMef+0oXLAiW2TU13D7a75zvR18gJgELFqqLqgHETMD5Jj7NSZKdc13YAcRTsuUm0bZhpcOw6e/X4dOftOEcPD9YXtfqFe6cMwan9tnkjVj87xJOpk65WrmDD35F0jV5ZuszMOHBpapx1upTezzG4LGoc64LDenrqQXYFErmXDjnV79nZqBYma+2OdL+KOZ4xX7my5RloYxiZfNK9Lg9cPLQ2mlnqIhaYcicw7HxrcO+hVl1qvGZHj8+oBpXn322QmvfG5lzAZPh3ReIqGu+zHkSc0HOLgEIIrHEcbjzREjeHt19mSBIHD/NkMkitlqbLWe/8sLfX+PEhDID1wWxLNiV8dsQ5mfOAYASLvYGRLK1lpcNRJryglgF09qFEUmKQ6Nk44UXRTaLdbTFOMlYxCiKC7qQx64FWnkngEjbMnnMHmjNub+vrueeg12uOn0WIeH5WhqtXKe1P/AllB16aCCICSDMKMQ50NK12DT2eABApjfmPbEInOqa2POPbC6PW4wBw6ab9xsDWWRoXVu0Pld/zlJjxySe2/DS4UhbaRzyhTAz1PnfR1EyezYm3ntPKAwn4YGzHjCfm6m+HwApLgky5+lx4+AMV0WNiGUhPdXXUbEtqa+9eR6IRYHbbrn889j9k5+YdyGNV15rm7JcCT4B+2ygdlzlOADA0aOPhmWbe4p3t6vzvmtQaw+ZTIN7fkJ1XBYT2xcy56mxY4c0cx5Ha0+CfF0OPHFswpZ7B0klDvnKH0py8YFuUU/f2aqW83U09+KlzguM36moC+e9rvWfxuKy63FijGDaabPVDLVgcrqUBVns46YPi3wPACqKHVSXprBg+AKcOuHU6AYJ487MEZXoXHMVzqn7XWCL2Rbw4npevri7Q30PTZdwbCW/79cfdX3EXhYfK048IVhWMnfu/sz5fvQdbbtD6kiJw+nbniHTZKoJUtZr0U1ZPGZXp0ZPIZ6WLQ9fJuGcVRVVIVdejN3VRG3VQ/tAa6cMu7LJGTITrX3VTu5ICjVsaL/9hkdWAVDFI+JqkuN8rT2dWZ4516LgOq1dp597Buf8/N9w1fqUTQLFSr3m/IJDxqBUqo+Jo7VXFGvZN4hBM+r0PbThIZz773PRnm1HSu49mSfzV94Vfb6Wz6rC2VPOxm3vuS2ybuQPf4DJTzwOYtvcUPT35W7fvtdC/AN1zkXNqClrIiPJOW+9/Q54ra3Y8tnPwmttVWrOAd/Iz5O9zue8K/AfEXGJu197Dcunz0DLbfweEc05F/e4qnUN1p91dsGHCRsgJAhwUQo4djRbpm4E5pFwn2J3O0MK9PnXHILDXvhG+B2S5/kpUBAOkDLnxVprQy046e7aZabeycfRJ/KYiZ0QAFtf5R80R1gR9Bxgti2pbzWRSxr0caUmKpQX2bdgaDjROWbE/30HOreybXc3/vbnjsi2AEAsW9FKCRCXfTNdljJzraMJ8rjw4vYX83/B8P43fvMaAMBz257Dzu6dOH3S6UinSyLbCYz93a0Y99e/BJ9HV4w2btfr9ZrHLUKUmnN97oZtY30HD/i0ZFqDa2fTvmXOaU98uZeMjocfjl/pucis4vPtzv/7v3C568JKaQJLQ5RhHShcX9BQ0NpNc8CK5/l4cOG1nJXnST3HRVZzqGrOhYPKaNiGa1/InDu1dXzcGao+5x4dUIlAadU7L/CVZJbka/WW9uLHmM4Wbs8+f5faMvHBm5eik5rHxwlzwx6QtHcMbFYVmwS68bx5yucwc84CWvyoGvX8FvltzE6fOxKex/D7k3+PHxz1g8i+42wcBsYZo7QIf3hmV8BStEioxH7EZDUg0NYTDXD+4vVfAABKU6URk0W8NiJBNeaWX2PUj38EMIbctm1wm8xZ/Xcz9jvnewHCOXdZ9OGWJ3CTQmFthUphodJT+8IKdXD90KJRSs25/PdbezgVLutl8ehGXrMpejgDABgrXBCOMVDkcUYMznnWp34z1wUcJ9awlbPTnVkD1R8G1oCPNza3Yntbb6SkR3aCF45YiJU99yvrqcXphTb1QBuG4XsLPoDlUk26UKzUaeunzx2JDy4aF3x2YqLRcb5jvoxJyqDIL4PlsgEzQ3fO//ztw9FbFU+BstJppBob+Qe/z3nv2zzo0vavfyUetyDUTkSSMjMwMOd8Q1NXUE4gZ84P+96jeOgt1UnLusnXuflPf0bHI/9F85//IrVS852aPGqlgBRwKgDpYn5PU35rmc6neOlG94u+E6LQl/l9ueznR+PAJT8t+Bj8pPg/hADnXHkQTvnE7OgmngtiJTvnvOY83Gfw2i4LlVPrRpYjLZeOEADT3xN/bsTuQ825fz20tpK6Y7354x/HmsXHIBPpFS2JPGrfic22EwZk/d+j09rloOIgZZ+qzjorsswmJMj6RwQaa8ZFto8gYIBEx5Dqc85Bkd81Q6C9KcHp03qLC5gcf0Blh7T0+irEfejM0OdxwXBuXjsfvy975DIAXKROzqY3XP45Zfuyww5D6UEH4bbTbsO1i65V5qcKTYDOJjamPP0U6j/96XAhAdJWOmRpac4PsW30Mm6cr25fF4gOWhTo7m7DyoMPQSFou/vu/BslgRAw1zMKyzHXjT7Te7v/V4EQc6dt2VwR3VBzLiAc8R3r+DPRMLYCVQ3cLtuwlBv2g11zLqjdL9+/Iag5zxdEHgqM/sXPlc8sl+1/R5YCYKK19wWDXV7QHyQ9C/nOb1T7lNh1VLJDOprD8Tbbq9oPZVVpjJpWDQCorFOdaY8yjK+LltZcdtTECLPTljLncTZzQ0URxteVorzICXSWkvD0eU/jhsVhiYxHPaSk+y1o7Y5lBX+ntLFwV0c06SbKM3vcngi7UNDchZ1VeuihsMrK0HbPPQCA1Uccmfe8323Y75zvBZSnuFqjKUsqGwuX3/5GZP0Zc0cqn9M+3a9r/afg9ajZk5c3NgGGVmqyA5jxMvjvJi4oow4yDNUmWrSGF9btAW8LGv6WBcMXGLaMvuQiU89yuXgxHkB50eMGwrhJrjPjwqMMd2u9H3MsjNS9uP1FpIbdB2KHWaL6duCYNxlsRuHOnIOnRs8L1i3f3s7PN5eL0G1sQgIBOyCe1u5qhoP4XaaAjQwn3yvKuFr77PrZKOlRr7kHWnCrMq+lBbS9He6eZnU5ZfjzCxuD2qE+4dMvA9/YlbhJUZ5a1Di0deew+EdP4PVNrQDUmvJtbb34xF9eVbbPSg7acWNOQATiOWMsoPeHomCUt59JAMuZxfpMmH/yOCw6axJmHDHSvIFOa/dcOCkbVl+or4CiRN04oQoT5xki856fOU/HM3gErZ16gJe1gUa//dTs98V/BwBqxsefXB9o7cKJIZaF9ORJwXLaa3YmM+s0GrQXnzmPDboQhH3aded8EGnt4X6i44ZFoNDa+wohCCc7iXIQoGii6pwnUW6JZZkFKWPsVNm5/vuqv/sL/fOYZqBMajhx3Il5t5my+u848I2f8A8m9eMelVnmWI5yHYsPOMC431n1s3DO1HOUZR05lVHgWA6chgY4w8KMEAFB2k4HlPwIbdi2gjZtjCBojWdToGX5UtAO9Rgmqn2hSOrUQNJpMM8zluIwz0N20yZt4T6Q7jVAMBEdwgP9nc29EcG3itpilFamI86Wk7LAGMPy57bh0T/yUqGhypxvWrYnsFfad/ckfWVIUHH88cpnlsuBUW/I+tf3h9YuY6ha2vXnHExZ8nyZcwA4avRRxuV1o8uDv++58fXgbz2wNOfYMUgVOagfUw4TPrl4UmRZXXmUcSBnzoUNpOvvuJQiZVtwLBKxUwFg+Dc4I050/agursYJ407AX07lLKMtnVuUffb476BlhUkpWwu2EtucdAOAlJUKTCCRea8pTWP3z34elOmIOVho5VSccHxkP+927HfO9wKOGXsMADOtPV/NeXq0KnQzqs1veZOL9vFesbMFplZqclBArr32mIfSg3m/aVCGk17LPwmLjKQnObvHjj02sh2xorQV4XQzN9k5L5aif1tbw+i+7JDH1Rh3Z8zG9qb2TdGFBuqtzTxQbdKyLQKSThlrvh3bUsQr4gThqGbgiMigKXNebIfZblkQzgTGKNa0rsHSpqWRdTnmBYqb+dB+3318f5qTOemqB3D1v97Cdfe9jZ3thdEpA9gO4Jgpap+a+ykA8eJK+dCZVe9z3POwbFsbWruzAaXrhvfPxQ3H/Ci6YeAQs6B8QDyjLJNB6x13JJ9QH2jtTsrG/JPGxfeONgjCJdGf49Dgt66Zvsjcegzwa0wtG1Y6IXPuO+dbnuLie91C7sFVo9/VF5wvfyn55PoiCCecHNtWDEraHWPo6nXl0nEi1zGmlVqu0wkVxvV3SFbdHmiWJ+H7lhXS2vtlsIpaWll4c2QYENLP/fVHDGOkgG2j8/HHI4tjg6emQJIw0AoYky6dc2nierumBmO2Poma1tX41Wu/RNGUaLZqWXajIjTnEEdVTM9TUpYEEXwg2jtMCAmDYnrm3LIhSTcETBCbAQetib4vtZdc0u/z0x195TxSKcB14dRxG0LWcoDrwirXHIJ+jD17AyKwLTLnXW1Z3PK5J5UgU3F5Cg1jKyLvj+VY2LOlE68+uDFcNkTOOfDOZMxlDPvKVzD8au5ksWxONJgekmMxj4FYpCAn1oR9IHEePC9OKnqNCiH1XH3o1cblJRWhPdQt1WHrgVHGeCmEaXwtcuyImjkA/OS/qyPLBJPT9VjgeOuPYtbltHTHtuDSqC6LU+e3ttS72kjbbZPaEv/kv7xc5tHlu8IOVdpFYwnMW4tYKPZ/36xRvJRqYkMZmm66iW9g26FN4F8fUhxfSvBuxX7nfIggv2xBzbmcKRIPlf/vxb97ybgfK53GKWeGzsSman/iZNGX9q5PHaIIwrX1cuNZds7lGm6Peeh+2W+tRimGtcZPIKJmOoi+IXRUa4qjQkEsyal0XaNhdPaBPBBxzOSpwbLX1ofnK2c/xxloPQBQGtPj7bOPfTa60HANbUbhagOiY1mcXpzLofOZZ7V1RHHOWzLbMfuPszH7wLtRPvWbAOEDsO48isHKZMTKYkj5MudUyn70lKgDtktofPu02B2aDbG/vLAJC7/7aNDPfaAQv7+/fc71Oxenxn7az57BOb96Dnu6MsF2Jsos7QiN+IDq3AeV4j7VnOvH7lKz8pGac89Fx38e6fN+K+tL8OmbjzVnzAXcHBenSsfX+Qm19q6d3IjPrPUNWtGv+sxf+T9EugcEgJ1QO2g5gFcg28C/D8S2FYeHZWOeHf1RkJ9p7T7F0dpzXTZvFQcordT4iewtsUQSZjclSn/SvVIgKPGOgzG/vhmAKqgjJ3YraAu8hFY9kfpp004kmFhioXPuB70Yw7bObQA4HVzZbZ5rPPX559BwxRUAgN+89iujSNtXc3/HlU+FPek7ch1q94oBOOcBI0kONNhcDE6M6dGacyt0zq2QmWNRcyyr7qMfQXd/y28TmBYklQLzPJQtOhQAUP+Jy4J1zPNglakZ+xHfua6fJzG0EMkOi1goqQjvpSdRhz2XwnasiOO9dWULGFO1gQb7tZap3aY2b3sTdR/5MGovuoizJnLZvZI576/43b5Ea7cNzrlVAIspHTP3yc+h/EzowRvqMeQyHlJF4T36lJ8tnzGiAo4hsDK2Nsq0EYfzKIXr28+dGRc3PbEmsJl45pwg7T+vOf1Z9V+M7tdfVxY3ljUGf9/yVMhWe81nM84dU41ff3ABPnjoOIyrNTMATLCIhS+eOBWfO3Yybv7AQbjj0kPx/gWh4Kcybgs/ah9gWww29jvnQwTRdgEIX1TZYJlwz78CwRoAeHLV7uDv0+dG6a5/ns5pfm+NFOIQhvYyxQS/uGhu8NnyHXWZOt2R7cCiEYsAhGqnArIgnBwV63njDZy+9D8AgJw/8XV7nP58wfQLguCDiuj5Cec6jtZuWwRVJSmkJGeX9obMgb+/sjn4O44+XlvWB2vGlDmnFD1U3XeRYwXnu/ljH0NpLowS2hbBP17ZEnxe38Vrtjf0vghiZ2Cluarws2vMghUmI1YO4tiGAIIMJrExXl7cqKxzmcupnAWg4fOf5/uTnJeH3toe2e4f0j0YCM6Zeg4OHHYgrjjoin59X5+/k1qlrd3dFYgN/vPVLZH1JQsOQvMf/xR8NrX6y4eBOOctf/6z8pmUhO8Tp7VTdD3/fL/3HwfGGDKr1yC7bn3ib1XU2mWIzLkwRCQnmNhp4IjPxx88VRI693kQZs4tZORezTG0dr2WPSlzHnffunenw8y5Zmjp2dKhgkUQZs59R2/igw9g0n8LC9QIWjtxHJQffTRmrFiO4mnTwg3kIBDzMG52lI0VnkzMb45ZLjN2LprhK+uLZX6w45mtz+CkO0/Cc9ueizjjhZTjCCPNoWbVfUpUxtSD6x9UHObM+vV5jxEHcX6WpLBP0mn8dulvAQBLdy+NXBtiO4GgKyMIrodFGWxt/Kq77DJYZWWKAKw8LuRFgoIxSXFx03YR8JMDXpogXPX73ofKU04p/Lh7EWLuTFkpFEktq2RHx8tR2A4pyHgfKlq7fk7vJFg2i5a/3cYDqUOVOaec+t3vaoh9wM9KdM4LYAQUUq4nt/XTr5XnUmR7XaSKw3fz8uOn4KunTMfFh4037u9Tx0yOLCOEcLo6Zcj6Tve/l2zDDx9aifve5IHRnEeDzDlg0HPyl++4+hplcWNZIx5//+N45QOvGM9n9qgqTB5WjuvOnNUnNoRNbFQUp3DFidPg2BYWTqxTExaSrRIsHaJA0zuJ/c75XkDKN0Zkp6t46lTUXGBunXDRwmgrib9NP9HPoIu2QNFb98auNxRnz0Rr/9nrP8PkmskodUojjpvsq/a8/gYAoOX2O7Dh/AvwwWW8rcwOjdp8xKgjlKxHtsWnyRucyqCjUc4FUuqx39zSin+8ugVtPTlVVE06qe0SdcaLiUQnqXJHEd02RV0w7UWvLy9ShI8+eEgYMEjZFjY2h9T7VGSQ4Od5/cMrlaVisMkrCJcn853ZFjqbVa2qger2oebcquQUaJm6/4m/vBbdbpAMmPqSevzplD+hobRwBeckmGjtssMu/tbLDqyqqijPy0t2zrtffhnLp89A55NPBsv6IgiXD06NxETxBeFY7+DXKgpBKOZG30dtS7N22+O+yrMjaLFSJiDjAsVV8bu0nMLpsiJzrr1bNGPOvEfKTxJqzlv++lfz6aUY0OUH1PS2ZYPYgijlt9tKj+WB0poLLwzW2ZaUOfezrEUTJiAl1TkHGB4V+/NEX9oYw0XpAc2iwkMyiOOg6oz3Rpanx5rbHh064tDwu8KECrLN/H4+t+05AMD96+6POOeFCMKJ99PxzEEWagGbOkLnPOtllXvHClQ9N0EEH0rmzQvPR8rotGRaohFEqeacEiCzneuiHLiOYdUoddvaSy4GoLZOnfJEtKwgDoldANJpwPOQE7Xl0vDHW6mF8/mI677dpyDl3oRgmDmWA0sK6MsZW8+lsFPRzLkJg52xlZ24fcU5B/xxn3ogQ8QAopTT2t/zmbn5N/Zx8HtC/SQnT7vavQFx71Lp6NiZzzl/efSDESZQXkjeOQEF9TitXS59K3JsfOLoSbGlk6mYZ7wkZaM766FLK/kUYrrPrtmDVza2BJ2G9Mx5LGsK3I6LC0TInYsiDEkSHzwcVhqd3+Q5XRmPxDu7lwLmexP/e79oH4RwzjuyHcZMqV7jYSVMEkGPYUN4cWf3TqWu3YPfGks7Zs7LIaUbnAD+M1+a4NwcMuvWYce118onihNmDgekepGjRh+l9qFl/qBkyEqLiBzL5SKUwje3tIXnTRm61nEl3eLGe8LvS8ZkXI1xr09T/dxx8YqZAUjUgKnI9USc84sPG68MCJ88LDRIbYtgWmPYYkhXpcyHfIJw+TLnTVeF0cy5z6viay5owTXnQX11HicztY/0vNUz5Z7BGJUV2i9ayJ2fT2lCKlZRUSAAB/i1XlortfSkScrzuvGDHwIAbL7sE+H3YvplF4KyI0OlUbu2VjGOhSBcbH31QOD/ztoPXJToqBCplZoRDp+cWV8CY5YNZDuApmidXOT44pnTVMe7X3nZuD3LqveCURo4ZboIlqfV5pYfw/VBSmqzwEu/9s9VC2IO4jtQftRRGPPb36LuYx8FAFSf9/7wOL6qNhCK4sXCMK+4uzkbK7txg/Era18L2Vq0txde1vwMT1x3L0CIMXMbZ7jJznVQgy2W+b+l1+PP3L1r7404RoWMWwEtnBkCMgB0sgchRBnHZYp/f5EaNgyTH+PdT6rPPitxW2LZQaDEs4CMy3//8FagNx2ebPq8M4MAnTL853sGZOTLnMvrZS0c11VEF/dlyJnzuCy1oLXLfugJH51p3N+gq7XLFOZ9yDkH/LF6iFqpUY/T2sfNqsPRF07Dez8/L+93ph4c9u3eFyjKlu/smWrn47RipizgTuWxs44w2teFwk7ZoB4D9Wv3C0Xc82vbBIyxRHYhECYuXH0eL2C+mzIsSluXgwhUT4QZbO+FIxbCIQ6m1ERt9+yGDeFXTbT2vVRqtjfxv/eL9kEI5/yC+y/AJ/77icj6rPYyxFG2OfwXzOC0zW2YiywNs0kEQp1Rnahd5ga1yLJibU6b+3VHzWYUWZfCLlPpgN2uVDOb8P5/9I+c/sJcN9KCRw5IdGZyoLnayPdbu0MDLG6gEcJfY2oKoQCanQlPe9HTjqUMCDvOOiP4O2UTfOWkkCrq9JFeQ/NkD4u6++/0uXAjNeelCxcat+18gmeBhTBcHIoNkeR3Avpl+/srUbq67JwLgZHKEq1ndVERoD3ngUPk3/OSOXNgN9QjCSwmi1sIZHq216yq5ROHC8IJZsNgIsiu2Ta6nn02djveSi1hTDLQ2vNCOLy/WJBfDdp3wvTMuV2ZkJmX4Xnh+6s5LZWnnqLc27IjDuenl5bOaQgz5wBQfsThgZMbtDaEoLVrnQPiYKrx7gMYsdCzYlVk+ZjNj2L8pofR/eJLqD5XVecfd81FBe07mH8sNXPeJbXeszRTRG6lE6ubYYc122/vWopd2uOgT5E2sWHJJSN9yAjPqJ2hfJYZT6mRIzFjxXJUnnIKSp3S4FiZFSu0E7DgiQ4qFvBLxjPhr04mINI7sLSqHSaIWvBCNAeSAmXcOZcCkv47wSgFGEPR5Cg9dl+EcM4dy1GcGFn52nOZ75yH6+OcqyGltb/DNecReN6gBhllMEoDh3bWUaMwZnrUltNRPbz/nQmGAuL8LZugprHUuE6HCAotGn2ocX0SejpCO092zvsiqmfHJPUsQvDH5zfi/qVqmeIuiQW7YFzI1uvodfHEyl2467UtGP/V+/GRP5iD4DImG5xz2Y8ZXjpcWUcMtjcBwaz6Wcb9K8FEE8tvf+Z8PwrFsPFhNlWmj7+4/cXItjqN5ICRlZFtAgQRJ37rcq0HBatc6qI7FzrKQhtNzpxXpCrg0rAWefzttyE9bhzs2lrYst3Kgv8LsGDnCmRcitKxtyrLU5JgEnW5hcS8Ulx6lNqqR/Rx5865+oLJ79b0xkojbf8fUr1wXM9GQdWJo/4okLL7u6VL7hkGOSINCKwzbANhWyRw/ACgLKVPMuZJWbAlPOYltr2Z/P07Y9flQ5Z5kdKFsbf+FtOWvBHddhMX+ep67rnEfZYX7RvOedz9l5GRBnShlq/fWlKUjva+9rNwwmEijgPa3hFt0SWh6eabCzpvHWuOPyEUZTTBsn3xpsP6tf8kBLX1eTIoFomhtQv4mfOC+5YDajba1MVCQuCU2xYqTgxbbFkVZpEZp1E1BBilgXMeofu6njESr2DNf7VzH7pp066qCvRFejK58NnMF/RLcM7jepHLYCBghhKYzWOOAwC4u3ahZNYBmLFiOUZ844sYdVgzSqePi2xvPDXxXOziehzi3gtHFohSio2Zdx/bOrch62UDNoFNgV3t29GbBsqOPy48rnabDmk8RFXaHwBd24tpA3j90dcDUOfE4Hi2E2TzPQK8umcJ39bl2X+BdmZmsRBCUHHyyUiNGWNcryDhXeRq7XKph89oC8aDfZPGrkOwzhzLUV5bkaVmLBTVkp8v27GMlOvta1sH9fxkx8qUOX8ns+mMDmHmnPYt47svQty7zuYMTv3UHOM6HSIAE9uFJQa7N2ktFG0C6lEurJfgnE8drs5/eptfAWF36xDvRGNlMSY2lAWaTr9+ah0u+f3LuOLvfHyyChAPMDFZ5Q5FVUVa5NRAa/eYF1/OJOvZDEDI892E/c75EOG9l88DAOwoX59XlCsnZfjqy9NK32wAuOAQua5PbMtfrN7t56BrwycBAFmaxSs7Q3GGScO48SP3+J5eNx3tmfZQUTaVQumiQ+E1N8OW3y/GIpmwqmyXsaZbNkRyzYejZ+v5QMd8nDo72sIp51F0PvooMivVGmzZmS4rclBRlPwCmvoxAgj6cevsg+m10yPbytG7IilB7RZYpw1wJXe5L3urRg8mdjIdOUc9PLsmvhd4qq0rdl0+uCRKayeOA6soWiNEUmo25oVGM/Uv7rrvbfz4kWiWT0dGEVwRLT3U5yK7dp36LFKGXT/4AYDQeCepFGhnJ9adepq51zMQeZ4LRW5LNOMvg9PavbwObL8gZc4BoLLdLJBFCAVLJQQMBa1dmqCtyoTtgbD+GMib9Q2CJLYT9FoFEE/d1ZfLznlErd3cOULBFLXn9lBlnASob6Cs2d4WCsIlZc5Hzgda4sXNiqZNjV0XgBDQRCWm8N5Wn3ocKsf2FizC899NWnDDn3vkbLRslE2tmao4v3KW2qMeTrrzJHzlqa8EEV2bclE41wKcS84Lv6f9nOsOv04NAhQQtHjw7AfxwFkP4OuHfh1zG0KHztgWFWHAIULjBCJq7d1+u1HHU6vARtbFBz1IKhU7BilIorWn08ht3Rp8FsKBYQtJG87IERh+1VX5j/MOIqg5J2rmPNvLf7uXo2CUKaJaAHfOqxqizLrSyv5K45tB8gjCJQXlhxyUDtk41teM774IQWvv7cop7c/kdTpmHz0aADB8Yp65T0NXm1qPbdlWmDlPCHL85wtHq9/rY0BEtGPzGINtkcC2b+tRxxe7gKB7hAoPoL5MtTPPmxaOzSZa++aOzbyjRh7Iweb+tJh9t2C/cz5EKCpx8I85P8QDM27OK8olO7xNndGJV+lpSBgYIwhrzi3A4xPNLW/egic2PxFsKgTh7lp1V7DMox4e2/wYdveE9Yatt/MezpVdsjIMjbQZ2l1SrVCFBY4Zc4z0yYbbPg+2ZSuCEAAwumMXNp57buT7/LzUiao0XWzcLm57gT89zzPAjjbx1BTVKMYVAGWAqJR8aM9kdMZQhghRAwu15ep3S8f9FiDxOgOPrdiOD/zuBeO+4zDutr+h/lOfVI/TaxBEY17BgnB6FumV4dFgBpCsir43cf+bUSV5HRlXNvD5vxE9B81AUpw/yTkPNnddo+OpZ98HDUIQrk9Ch4UhoO/7z/u8JT/H6C1PAAAqJeOVwAMrCUVaqt8f1kUDAIQgjDxR5jM85YBlTBYy3H+YOZef07g2aPpy2tERTuK6WrtEebdKY6iVNePN5zNEEGU1hNLguUpkNxjaiAEIzrNoUv4aYgYLNMEcqDj+eGlj/xoWOLY096qlGiLQZHRgAVyz6BrUFYfK8QwscIaF0/7opkeDDK9F+X/UAv7d8nR4GO3nVKQrFIeIJIogcoyuGI0xlWMwt2Eu/nLqX4L+xZ25TuP2IhhqKlcilh0QwqgFdIEb5SmPn79AVUVUJJM1cNopSaeM9fWR7ZME4XTGgMicB+KDDqY89hhqP/TBvMd5J+FSFwQEtmUr0/Mb/+VCdyKTqTtTVox6+9iZCd0K+oF8tPa+EI0GG13PPoueJUuGZN+MJjuVcfjI9Ufgwz88YgjOqO+QgwtOykLd6HLjOhljZtbi0zcfi7Kq/ErtANAwtsK4P545Z3kz5zriaO1xEJltShksQgJqup4YswpxzinD9Ea19E5/Br52yNdw/VHXi6NH9rGjawdWt5g1aOSApGyPjfkV731ulZlbK7+bsd85H0LsKduKrNObN3OeMTi8Moqkdg5ja0ugi8GJnuL6gy3o7K/tClW34+h4AHDcEtk5ZxEjoMcpMjrnw8uGR5albCtCLf/Aiv8gJ7VCknHlnW8qn8ukfuV3ropSuzMxhvmWlh7/+OE1enXnq2jLtkWohmUTf2rchytlccbV+QZ7TJTZo0z5nSWGZIxTsTSyTFA1d7X3IK72PQ7pcePQ8LnPKct+0HxcZLuNnZuxvq2wdkEVx6nff22YOdsWJ8S3L0IWGaRB5jz5OySVQnrixOBvgBvEAiyXUx0VH05D31Xnc9vzBxiEINyQZM5F3an//DpeBiW9XOFbVvK2wMAoQdkIzgqp/eAH1P0EtHZ1/EiEHACTf9vOZUC7el1IIM5jq6JwnjnjLtfTUn9Sp21t/jrtOrourKJiDPvylzH+E7OB+78Y3aHmhA51H14qOjl4XhhoMFGNP/Mq8Lk3gIlHA5FymhCFZMgYIWAJmfOSgxZIGwvnfGDmg1xzLs9LJU5J5BoLCrO8nSwIZ4vMuRNmuZi2j5SVUlWR+0FrL024zkDIADDOs3LmnACu/1g5nkpr9wxq1dkLT+fnPBiZc40p0nIHD86zDA8WvFto7R3ZjmAeffuZbcHy5c/y8SN0zjVBL00gTmCwqdjy/nauj+oI0Hcgcy6EJ4cSXMis72NDSUV60NkL/YV4ZkZNrQYhBCd8eGZk3UAh2BR68KhjTy9WvrgDLE/mHACuOCG00/p6yUWixWMMjkXw+eP5vg6brAapCsucM6W80wTbslFZxBMbxJA5TwKVnXNp3C6dPx8A0PLXv/Vpf+8G7HfO9wL07GVOy3Tka/914szQ+R1VU2SoxzbfRkEHnF0fttmJo+MBQLnEyGaUoftFNaNrMYrfPhN19vS2EeVFDhyb5BG2089V/Vwq1TZf/8r10HH1PcsS9ycc5j09e3DJQ5fg7T1vw7GciDCFCS6xYBVvQumEG1FV6tcqx4x8I6qKlUCASX09Xf84jp9hPi4FNVJ8kmDKolkGgUBGgOXN5mCIDrmWF4iK4gm8EwZFf1FRLGkhCMHofI6V56Ls8MNhVVYG28rGbOcTTxi/FtcWMQlt//pX3m2IzQXh4rLEA0Hzn/8CAMhu3BhkjYk/ZnhSSQAhHhhJo2s7Z7MUTdHUVAWtXZrEay7KIxam1JxL78yvDgNumBHdHvAz5zKLoQBaux9gdPyWZbrTwjwPcGzUffQjKNp2d2C0E5lnHGmJNbSZ81K/f/wYrzNZEK5+MlA7gQvWmbLn4rxjnvkpB8tjEgE1BF7LurjTkxoutbcJBN6SzYeHz3nYvMI/n5++FgZH5Yy2KZgtAs1K60n/PtgUcDwGzwb+vDLeSCOEwCoOGVn5tBZMyNciScz1ep08AHTTXoXW7lk8LJtymeqc+5dVnquZf61JKl1Y5tzfxqqICknqzjltawOjFNu+ciVfn098cB/BbStuC/5ub4rW6QfOj+7gEMAyPLuDLficz4l7J2rOSRw7aBBBKRtKWY69AnHvispSymf97/6goo6PQYyag0cCvV05bHhrT+K+5DrzvmfO+b+eHwQQDF1dA6uQmnOPMhQbesLrCMQ9+2jzxrZSE+t7+98Wc1/Fu/wV2rfx11P/iltOuCVibOg9/+RsZG1ZdPI/cGyopFjkyJR2ju+fbe4nKaL3B9RzRfZJVZMSM+cKGMPum36lLIqjtxBC8IGplwW174Bfi62N0GW5wttBybR0oeTrVLwFu2RDYd/3Bzz5WqeslJHmPap8FB6ZFdJiVu/pRfHw+2EX74Tr+DXBMfWVhBAlc25qlddAj484tcJ4Y8xDXzPngUpxaSmsufzeWobxM08XNgVEi97qdfdPfZmXLuwrNeeFQGZ5sJjMuV2nRolZLhdVspUmg21XfjUQzxswCslQ2VwQTs4u77n11oQvFI7Op54CAOQ2b8HIH/p19v744MrOOShY1cToDoJzFLR2fo1H/uhHaPj85ckHb9kQ/h0ZV8zPGLEdpWVdZvVqLJ8edeTl7Lig96bHjfPXabR211UyhVXju1E7M4eG2VLt2xCrtes4fhPXDZn++L8kQbiEY1qOGuDwcgBjGP6Vr/DPMU7oiEmhSA8jFpa90hbZZkapQQRRzCF5aO0jy0eqC079EQBg9p7/4rz7zlNWyTRxkwMs5i15/lrfwd9Dm/L/PIughyRnlWNLFwpEvhZvIqBnCoLfufZfwZjsEQCEwHV45rxSangiKPFPb30a1T65gPq/u1Bae9NvfgMAqDz5ZIy+6Sb1HE1q74yFYqBDHHzaWxDsH935YTE10YPdSi3f/t6JOPfeYEWw/wlBOD8YRkLVdn1df3HKZTxZVlLuO/4J16pjT7LTKZfpxQnCyUrsMigLM+e2ZMdG2bH5H9QcpQWJMIfjp9nmnRhjZ2TXhvNQnEbMuveeYVz+bsV+53wIMadhDhaNXBRxCCOUvT44PN25nOJ1ffSICTh2WlR4DQgdxazHDZZipxge8zCqfBROn3h6sF395z4b/TKjkfrMJHrLlYs+g8c/czEATjlPGTLnC3blF8363SWcPinXqxNCcOYvn0XJ6L+gdHxhqthioBCiMYDfD9UQHj9v2nn4+fzTgs87ulyIASnYPmEAVZxzQ+bcISWxdHAGGlB8rlp4Fc6Zck7scQRExqdo2rSg/Z1riERSy1coLgTapO1JzsAPz5kD2xZG57vHOZdLHzbt4davXnM+7IorlM8slwOjnqrqrE0GPa+9Hj1YP2jnekAEiLa6I7YNuC5oJgwy7br+R30+lhFBPbMV1tf7v8PNyrR2is7HH1e/K9dh+1TiQO25KJ2fobD0n+Hf+a6dqMG2LbTfc2+wuPkPfzBvL2fHxW/072HX82E3AtrVhe4XXlCcHcsGhs/ZDVtupbaXae2efzzLzUmCcAlGtZUCwPh17GkBrqsHnrkRtbMtzPj3z2NZP9MODdu2gRBkrKjjWv+JT2D68rfVhUHmvO+G/m7/mX97z9ux25iUzoXDKtdyb+zigVOLArbHae0Vpea2TZfPzxMsKhB6yzcdYq6nhrlyd2ZPUAcv/s3ZwMLmWnzygXB7F/w9+uxj4bxsbeJU7UJp7dn1G/j+d+xAxbHHKOtMxq3cwrE/jIJ3GvNPioroxdHaiUX2Oq3dhHckcy6xIkoPPnhIjkHp4FG/3ymEDrNgTg5e5rxhbAVGTasxigTqKC5PFq2U7eQ4Jz+OwRp0DKJcEE5sp3fC6XaS9Z/W7u7E65taIxpTJgS+kG+vXnDIGPziwgMBAHXFdZg/fL7xezu/+93wQwyzJ7Mqv0jwuwn7nfO9AD3ark/cclY1H214c3MX5Nvm2CQ2mv/aTl5rLhzUYqcYHvWUVmoAkBoxMvLdt3YtRcPlqkGTj94iHJ+c57/s/ZjsRGJrc0uYSmCM4I3NrX3ajzh2rxtGHlO22Tl3LAdMWu5aViCfKyg+XnNL7LEUWrshc24TK6JmKQZGCi8YqGxiFyTgFqpX29jaysVvnkvzNhgPHRkyABiAg4YfFPm+eZ/qdfGk85g1qiq4nvtKzfkHDh2bdxu5XESou+vvFylWxVtMmfNIKyqD2FK/BOEMRvDY3/5GXeDYYJSC9RTOOikUwuEnRcVBRkXQ2mXD0e02vMefeyP828+ci5pVUzeACFLShJ+vR7dwhi27IHVWme4uthcOye4f3xCs2+M7971Lo5oQxuOLj0PcysUVFGZG0XLH3/nfSQ6TCKx5OWDbG/zvV/8A3PMp4K/xwb50sYMTP3YA6pqWKvXmqWwHxk7nWfVU2o4GI8T96oNzzhhDDhTdBXCHTfPZNp9eL2fOPZ8uZPlq7Z4NnDnD/HvPmDQ4WZV8mfOkmnNKwhyUoK67NtC4WqWueunodaX+fERSKUASCiwUtRdfHPxten4za9eG6wfQYm5vQm7FN3ZmNCgTJwjXMLbC6MgMtkO5L9LaBQPQKi8vrCVfP9BfQbh9CeLeCXNBLoMYjOfEdgg8l+98yWObY7c74tzJifuxCqC1x2W0hXlEGb9fwsbrzKjz8Yt+5x6nsREmHPfjJ/nxC2CUBX4HoTjpgOG45r1TkKpcCsYYKKMF2b/dz6vltpWnh4nG/yX19v3O+V6A/sDJDiOgOjxx/u/jX1qMWy9eAMtiSs15yrJiH+g/vv1HAGGNu8icu9RVjAxTBu/Pb/4etqZKnU+1URkoLAIngeYy4jvXGZeLzOzO9jBTSBJb/JghBiSZ1m4TO9Y5l68pd0z5efT69N7eFVrttnSj5MFPsBRkWITFOrUuDWntjuVEzi9zJI8kNsstLYXxlHKCfrXF4Mv2VErXipCC1dp12r5cc84DQCJzvm8MfmVF+Q1Ik3ihfhesErWlDsvlOPVZMlCz6wsQ1euPoSV9x26ox5Rnno4YzsR2wDwXtGcIaqqk1knCASWGd9y0THFYfdp347XfROXpp6P00EPzH1uOxucrtfGfPeLYiUJX4e5kiregA0tCYf67W3DAI23upz5UkNXa2+6+G0AeQ15kmmkOeNCnsrcWVnoxZcFw1C8+BNmi6mDZYS9cjZM+MRdHvH8Kxhicnv44539Y9gfMX/4LXNYY1q7HCaWaMucX3n8hAN055//aTNDagVxMLaM8rtZ86INwRprZZvkg9nPYyMOM64PMuYG26VlhxpwSYE79nEi7tzuOtNA9dVTku8zPFonnOC+1PfAqiPI9/nf0+rp7pABBgS3y3mnMbZgbdGDR26UBsuCWSRAualP0tT91PugOqn7MQjKng432hx7kx+7sHDKGBPUoyLs8cx4kTwzsi8Fwznm7ND5GrH0t7Jyk6oDkZ1/IpxJHay+PsZU8OXNOQnv9jU2t6oaE4JExC/Kq6a7bHZYmvX/B6Jjz9ccxeLAIwW+X/hZfevJLeGbrM3CZW7i9KsGRShP1VqnvZux3zvcCdCNkyU6VfuEpzrl5wJ5QX4bjZgwHQJUsh2OT/H3URebc5s65x7xQmAEwZvBy2R5O75UgaO1uh7nNlhy54y97Qi0NcdDRGzUw5o+tjixrz7VGluVjlpqc8wfWPxDrnP/fmXOCz5zS7d8Hv4Rg2Be+oHwnLWX7ZOf8R69EKce2xaKZc3//Ozu6g8y5RazIvSwqLkNmZB1+dZqUyRWZNdtBTaoSFd0MR6Z4dDNja6UIBRpaugiQK33PImSfy5zLpSBxDA1TFwTxfo390x9Rc+GFysAO+C07Ipnz/NdQf1cKgWwwW8UlcOrro9s4NuB6YBnVOaeFKDbnQRBltuyAEkYMjjIpUHk9PXo0Rl3/Q1immlYdsnOe59plVq8BAHQ89nhhvYHlmnPhnMsMCX8C9zrM7bAUlNQAJdX5txtECNYKkQXBehMCCaImPtcLNPWd2mdr98umOaSLHcw9doyZwh8454UbUje8yhkLW6X2ZadNOM24bVVRVWSZcMpl1pnjCxHalMH2fOecmp1WeVxtvOoqTHnssYLPXUaiGjvCkgdTK7XD36aKIFyP1xO0MRT490KCh9Y/FGXX+WJN7ffdDwDIrFmLJBTP4HN02UIeKJPbxpky59u+9OXgb6s02gN8X4RszA8bV4mTPj4rWJfLeqFjZcqSG5YNNq3dsi3MPiZ0UmxH2/87MJXKtbtx9OCBgv4PZM7femorAGDDm7x7iaXQ2gfuNm14swlNm/n8IwKvUw4ejuMunoHGieH4l9c5t1RfwITYJBljfsYaSuZc7nIj4Fp2kAiKw/o9YfeNH55r1sES9uis0RX4ysnTsb2Tl+s09zaDMmq0z/NBtqO2X/X1gBH3bsd+53wvQI8GXfpHtW2Y4pzn2VdR2gVoOLk6Vv7saJaGNecZL4PWTCte2B5SQ0wiIbaHyMsYZM7jshPS09TakwvUH+sMIndf+9fbmH3tfyLLh1Wa61tIKozsHzK+FgePN2R0SAZFjXcBVi+2tXFjtsdVjVpdjA8AmnuaUVUSGu8usUFSfIAi/iuSGjEC095cEmyT8mTnPHkAtSwWqdUOsneMQmTObWKjI9uhbFfMHMC2gyyR8nMdBylm4dafeij/Fae/Zmz1OIVnznVae/iZMiZlzvcN5/xWQ9cAHVlDFwRx+mWHHILGa64G0TLntDeDtnvuQW7rVmlpAYZGP/qQ2xVhRrb+E5+I2Yj3OZdrzgGg8/En+nw8HanRPEOXHjsGXpt43qP3l/RVsLAQ1EuK73mcc9dvOdf9wgsFRcaV2jPfObeKw/scZB0LcfQrzRmAocRdk48GAIxe9nKwjHZ1xW0OtPttpB6+qn8H7KshLZxzXSjPgM/P/3z8bvrR6Fl2iuvKePtC2+PZc8+KdkIR6I/RZ0JSTbm8fn17dHyauANKK7WeXE8kyOxZXAhO7w3/780PY/YfZyOzkuu2ZHQml4bi2Vx0qvLUUwAAe279XbDOWJYhq+XH0Ff3NXjUU1gWkw8KWRmdzb2Jatgmp2co6qTnLJacc03N+p3InI+8/ofB30MlDldIC7B9HW5Gfb/JIGfOTagbVQbbsRS20taV8eWUgEolj6OvxyUvKAM6fAr7Kxuag+12tEdZeuPbd8DdtQtuc3NknQBjwGeOmYyfnj8vdhuRFFxLbsKKjqewro0HiyxigTJqTDTmC8jLyZP2++7Dru//IHH7dwv2O+d7AfoDp/f4kx2efM7P8GoCRotRU8onJce2ErOjT295GjkvB4tYSFv/r73zDnOjutr4e2ckbe+79rqve+8VF6opNmBq6CW00ELgSwghISGEBD4IkEbIF0oKgdAJAdN7B9NdwDbuvdvrtb1F0sz9/pheNdJKK2n3/J5nn51yZzTStHvuOec9EexoVkJo1jatNRq53NR9ino6vIFGzrkMBtFRKsfsOd99IIqwKOCu74zFY99zhrmac5p37XcazADAZeN3EwuN2sfhEHN9sYWrPkak6hNEat5GxMVzDgCb9m9ybLd452JL5y0uiBBCTq+aEImg/LjjAAARk4cmkUAUE7ijPIXWuetZWahfDyEhhGdXPau3eeM7byAEAQiJkNwesCHRYRRGbcZ50E6pPcRNNm23Y1+brp4flzm27G3BuX9bgL0tiVWDOwK7yJuGW1i7I+fclk8utzTDjqu6MWxlilIwNMyGZvEkd22A5gULEF21Co2PPW5ZHl3joqKdJJUnngRAydkK91DCfN1C2PeWNbjvoKIdOYtzTREmAStIiNXVgcLa9zxilFjSc85NnnPNOA8U7eDxbK0671yUH+vu+W0vH/UYqU9rpY9C9T5h2K2Nyv817wL9D07685LubCYR1u5WUkzDzfv84ZkfurQ0fbTJI93apgxYXPCarHjORW/PeSrhkm7onnOPa0db/9eFTuFSzqCP80kCQ0u8BRVN1v1oYe/PrXrOdXnxFEXgM1RX53uccrP6HFOf24UjTddUAs0E0aX8Wi5i184x8/X7myGp70ZXQ7yDjHPzZ4u2+vXZyDkP1RrXTaa0BWSZu6ZK5hP2a8HqOc+Mca5dHyHTIE60xX8w2vwz26sjaXgb5xzfblWcQR+u2uUZFg8Aw/coaVIHPvjA2F7maLj+BUu7a48eihPGOdNyjOM1nsPXvXsdvt5llESOy3H3/qptQF4beNTJkzScZMnvOyhPcIQqh60PZbNBXlXsHxJaXihiZM9KPec2kef8ijeuQFSKIsSUfGats9SnzOhYu42gVoZKse/11y3LRL2EjoxhVaMcpXLcjKRTJvbG4O7Ol33c9CB5fek212OPNRrK1UW9HzaOQxDcw6u5KmoltGFSQxUAZ36/G/ZcdPPAwfo9Vo9VyUHKQMM/zhmHl66epXws5yiofxpiiXtIqcCcnnOtY1pbFrKEtZspDhUDsTgginrnzAwLhcFsD65W0drZS5TyoO/LntpgOpdjelfojnVJ5rj7zZV4b8VOzF+4OdC+M43X2IibcV5gC+Wz5182PTffsY1Q6p5zLO8zohzsJbqCYKnT7TE6LO1xHznf8Yc/Jv15zgPQOq8CSg9RjLqCBqfqcVthlfv2l7wFXJxaeDAKTaHLiQThVHr8+maUzpqV3OdonvMCIyJHGxSJ9A4wuODx4q//2c/Q6640qebbiJmexyWqonLJVJ+qCyvVc7B/KzDUZcDgN92dy0x4qbl7koRae3PMOdil78ZlIKgsYrwrjhtwnGP9u5ve1ad5TIkI67VbEYSL+4S1B03vScTo2tEYWzcW1025zv1zTO+O/7lExN3HG/PvjjIU4WQB2NVqFYKTAXD1YWbXLtEKtJRfcJ7yOZWVvsepeY+053rNxRcbKxMY55G+icU2c4E4j3ufV1nx4ALuueQd5Tk379NhnGejlJrp/ZepevadIazd1zhP83cLFyjnobRKeUeFIsZ1IkYSCVAGCWt3Xy5xjkrV3pjcUKWWBQ7+3WK21J16j6hXM3ZbZWSNMmgYFsOIyTG8s+EdxzZ2fQ2hpMQyn+8DQV50zm+VY9gvyNG9rcaqJsxw7rR+rl5ma1slX3zjHiVcOywKCb2jD37zIKJy1GKoXTTqIn3a7eIWZaDls88ty3TPOZMRcnkp+j20Ft90lPV7mH6TTXuSU6IOCcwjwkD5HgVhwwj7z4r/BNifVYhNsnRWbZ+jfu8R3UsxvIcimNcmtSFS9SmK+/7dtUO5nj/jeJBpnhfZFNZu0QEAUBopVQyJkIi4m3EuOj3nbYJ1PnA4p484jKK8r3rOJa6HGX2xzj/kqqPwMs7/8rYzL7N/re3BHsR7EOR9lYJQnrlmdzbQBxREEeEePdDjlltQe7kzvJ55eZhL64DewaoBOBBNg5AHdni3gxGhUDRhAlgQJXgTuue8yGScqy/7UK2iN9Dz9tu8d5Amj2syxE2fuf8dZ2fFQdPGBDv0H6BsOZCkiI4WOh7A4H1m5TOe69wqW5j531n/i19N/5Vl2efbjHdSzJRuoeWcuwlyAukLay8MFeLhuQ/rnUq/z9lUy/DeKOPh8dp4QX+U2IXgAKMzNqZ2DO756h4AwIfDVdVobTtNRTpArXNlp6o+ickQ0zQhIg0NwfaRo8TluOOdqVHVo1jPOQ8qTpaOXGLHPk19InvN6mx4zuX9pohACmv3xD54YzbW01FKc9LcBr1fMXy6EhU1cIIS1RAKG/dqKOx/TQbKOfcYfJUkrmc0nT21n9424nMfaCljnHP874vLLOsSVZpS9m9cc8WhYlSpA//PrVQihVbtdfbZ7M86czogAPKcE6ljH939bP0O/OZ5o86rpoB9ysTe6FPtrDVrRuKSpayD/YY8adBJmH/ifPQqdYaWmAcJLDeBV865fZnW0QV39cj6hcXYFSNl03f405srXbfhskc4MWMWz/lGreya6l6YN9bw6H+27TPPY9IICSHLbxO3dMjtYdDKOnNIstkD5BbGGcNex2CCVg99S+tyi+f8hqk3WNpxSfLxnIecYe220xI0nNPPeyYKTH+Iy5zrduh/vnSmCOQdyXoNPTCHSPNYLGnhMrHWPUy19PDDLfP1v745tQN0w+Q5B4DKU05GqLLc0Uww5/lecrFjfUqIIaCbauC87WMcA7qLiYkiul13HSrPON2zKSsqQtmcY4wFLp5zrYOqvfRLprsrbysbdvyLf/5VSUYHmO9xD8+xHysXGQJA/de84NNS+wwtrD1xzrlX+DcAvL7+dc91GicPPhlhIYyTB58MwOoZX7rxC306ElfC2t00RQDnwGemaGxr9FwnCXqFTlcNEY1FOw1NGrlW6bxqnvO4ZpwHVCXWI6JMg69aWDt3iVzo9/BDgfabC9irzpgpLAl7qrV7kQmD0k/QK9A7Is1YVPszpdYuc9c68vlEYYmtakqarw0mKFE0XOaQJI6isrBu9Ju1CfqOrPHahdLWdFxeRrVXvzwuc12DR7v2QwJz1epZXqlEmUUa+gMAdu6P4p8frrW0CTLWVBI2nCPN8Wa8v+l9AHC1VzT2v/++ZT7cy6oDQ55zImXsBhJjEh4wCVpp90KQuuCSrHjOTx6vXMz2PJPJ9ZPRUNHg6ikw3xjmY3ILb6pf24SisVbFRbMgXFHYxThPYkRRCvD0ju48zHW54jlXjmX7vlbMvP0t5fNLFCM/bFdFTYDABG/PObN7zrVaOMrn72rZpT9glMXuHtS4Ledc8xptbvsauiCcIKJAtNXdjsfUnHOXnYZEMJtoX4ttPCNwrqXJg1w+73jLKpExMMbAmGKc26MAso1W7i4VgiiLlx2mXIc13/ueZ5vYeqVWqbR3L5aNHoPdf/tbwv1qYe1DPvsMYmmJa5uiMdb8KkfN9Xage87NNVxd7uFwmXENFQzyr7uaFBXqSzaqpo54iHmZVeUjvXuhx003ee6St7Rg30svO7Y155yvPk65vnlMNXD8oieSKBeWLkb3dqqV+3LEjcr/msFAS/uiWbpv+wQ1F1/kvlKWgX9/B3jiXGU+wG9jDlNPlbqiOv15aX4+ftDbSDkqiCth7V6e83R4u4Jgf36bkQVY1NrdqC+xirFN6T7ZMi/5eM7jO3Zg/cWXYN+bbxkLTZU99EVamg4Het39J+tOshFrnSISlzzTtqS47KvW3lH4ffbeHclFDKaDguHDjZlMhrXneSm1g88YAgAorTbu5/oB5Zh9wYi07F+7LmTOIcUki0Fu9pb3H+Os4GJm537jeeelyu5lV0QlWfd2a00iIfd93DNWGRwVq6ss7c0EGWwqDbunCM5fraQTDqp09i+aP15g3cehh1rmo+u968TnM2ScdwD2F0iobAkA4NmvNmHYL17SPb9ewlZmJC5ZDEn7w19TJ9+wz3nBmkeZLcfk4j0c+OlmhHpYRYgELoOFd4OFmlxfir6HbxNyCmKcg7sbTqLIsK2pDRN+8wK27TVCxcLlyu/q5a25dMylmNBtgmN5XVGdp+fcoVSteixalykhPRe/ejF+9M6PjNXc3aMRt4e1m8WQ1AEATRfAuqEE5qnWHnYY56124zxoKTXTNVAwYIBtH+qILlPSCZpyRAguHYiVlej7rwcx9PPPUHHSSa5tCkeMwPBlS11DvjUan3wSABDfqZRe2X7nXQk/W6vH7Zv7Zzt/QmFyYd1+xDasVz7fXDbO5TqTS00GVjpDISco+bMYOlf5v/4j12ZihWKspjRCrqu1O/PhNO+jLpBVPcDRJi9cQNN/oPzftQJ4z+O62524ugEACDyObtde677y1RuAFaYKG/vdtULMjOs2LtDnAsCUeve8+rgcx84W5b46pPch+nJeYH3YSSLwweYPYGdUzSjHskzh5wGKC4YH3C2sHXB6+COi9Tu+s1X5fm7G+a5//BMH3n8fG6+4wlioR52YrmPNYyrLzvsijYMYLfEWvLL2lcQNUyQuxy1q7QBwyFlDAQBNO1scnvNTrpuIedeMy9jxuOEXUv/xs+0X9UwWIRLRz3/b8uTLLgaBy+n3NHc0muecmXLaTrluEoZOTU8lA+2a5BJHPCZbQtmT4cnPjH6+lxE+d4y7mGjcZJxrjrVCj+PQSuvq70yX50SQsHavQdIDMWWg9cpxVzrWaRE+obo6DF+2FCXTplrWxzaScU6kiN17Ga74CoCMW19citaYjF/NV0Lcg5SpkrikGFwez77nVz/vua3FO2wyYD3DmySroSlyGaWDfgshvNfVM+8X1m4PwzOrgc8YpITuXH7oQH3ZLScpHar9K6537CskMDTFdiLW53qc/uIpjvUr9qxwLPvH0f/A98d/H/83+//w/EnW3+ikwSdZHhqSySAqLLPe+C1LFHXJrTcpuZBr9661rPcKq7R7zi2/P1QDQnDqByg55yF341wUIUSNTlq0ssThkUlKpVjzINquB+23EQSGv7y9Cq8v3R58n3lAyZQpEEpKIFZ5CJ+pmPPTvdSS13/3guAfrNXg9gsvtN2D6QyzbnzyKWXCHPLqcg+LJaYayen0ttSrUQG71LSWuLvXs9+/HkT9Tb+EUJR8/WUtOoD5GefaeXX7/HzIZwuSmrHOabRqVNYaBo7glwe+22ZMBPCcs0CCDQqT6ye7Lt/esh0fblZU3M156kOqh1jaeYWK2w3cTOKX2x4XTca5RzP7oLdo299DK5RKBHrUR6LjUdX+zYNqevQN5wgmqJEad3x6B65951p8tf2rjOw/Lscd77f+YxVP44Ln1uC9xxXjUzOE6gdUoM+watg55tJRmHPZaMfydOA71pGtIAX1vbPvlfQPnHDOwTuBIFymx2S196wsc0gx2eI5T6bEXpFJMM6rlNqEvlVY+MujHMuf+GyjbnMYfTz3z9Gdaeq142arpKPMrpvxXjRKuTerzj7bdZtIf5dB9U4AGecdgJuXOVTxpcNT7pbrYUfiEkQmYt0uxdu+r9U6gl5ZUOl9HKZR+fX71uvTXi/6uE0pekCN0Tl2M/r8wvLjO6yiT7Lpux82VKlPajbOz57aD2tvOxZccubgiwJDuPITZbrAaSjWFhmhQFPrp2J8t/GYVD8JAFAcLka/cqcitfn7mL36Qs2Llna6l1HzyNme4l7Gecx2bjUPe01ogJ5zLjLR8btG168Hi0uWjuenW5X6xywcAosZRn6ouc3hkUmphJCHQZKr71u/QaFkSOiZNRnn9us50XI39PvOxziXW63XU8GA/oH3HxiL59zFODeXfkxTjr7yYepnLXkK2LMOeMc99zzSpw+qzjgj0C5LZs5EwdChxgLZz3OuPDt149wtJDoLYe0ZwUcU7qDDjcgI5lfWzt5jDWD0JhNOfvHoxHoG5sgkewi7a7lJBK9YkQ78jPPSokqUFiiaDp6ec9uxCkXK+0/TEpHUR4XZcy43N4NHo652tjbw56bSHd+xw2E9Fo0Z43n8ybKtWYms8MvDbw9updQKio15TYBN8HhmVdUrv21V9xIMGOdfmi5V/MK7s5Fzngoblu3GPZe9iX27E1e+8astn0/oz60MfQ1t8GL917uxZuFOiKbfKxmhwOuPGaZP+/WDCjzC1bVL0BwdaWdAXYnusNL6LG5e8vZczjN7zQQACC4mqRZKX3roIY51ANIa7ZNLkHHeAbgZSIXdn1WMc2Z0NrqVJQ5ZlWTFONfqE369ucmy/vvjv+99HCaj65j+hmgSj7oblA61dlNoups3gjGGo0e6l+3Z9ptbrPvism7MayNursa9S2i7yBgK6t5wLC8Uyh3HJkNOqNTLwa2pAn41q9UXvfZitYeNL9iywLFJpdjPWUpNNRoKhAojrF0IOToSclMTwl8stXhaLnzlQqxvWg+IIQgmo1+IOgdZ1jWt8/4udtSHnOwRupyMpkBHoIkMHm8K23rooil4+ZpZGFaffK6r3GJ0PoomOlXIGWNpfRFwKQ4Igq8YX+uypZb5gsGD0/b5GmYDys3jUVVsDNKltTZupVqyadQpwJPfBTZ+2u5dsoICtC1fjvUXKnnTuuc84ny28lhMOZ+iqPQsDrhEhGRBrR0AiqdOTdwoGd7/g+eqcKWRB1h13Fzvfdi96qH0eqSDGNExky5Bq2Q1Frw85/bQ50ziFylQXlylG+d2KRMN+7GWXHw+Hp8l4M2xyn61qh1m43z5hIlYc3qCwStzdIw6zaNRhHtaQ15ZAA2OoGgdbbeSeelAc1RYPtNlgNXLUJx24kCEwoIlrzjdmJ+nPQYlqSWRYarPPy9Qu2/eU0qmbl29N0FLw+ub72HtmUb7fV65X0nFNJfZq+7prj/jRnVJsPvVy6uu9Uu10+WWWnvWlL6Iq/3jNVsbLduZmTrAGZUSFE23yTUN0x7hZifH+qXpgozzDsDNe8DEKLZJn6Bs2M8xbYSST5dIqR1QXnQiE3HFYYpwQp8q6zblEafasob5RXZMg0nROKBqZ8gUh+WlfmuvI63RsnChZb4s2qzvTVNe9xr545L15elVMqJHoTKK+Oo6Iy9SkqXExjnnlocC84k3072rHp5zM5eMvgQN5Q0oC9Ujpn7HC1+5EPNXzddV3SUehyYIJzDBco541PAM2TuezfFm14dVTN38o2HKb7SmKViuKWAMEnMPY/FA1OpVK0xQ5iNTLN2iDEjtb1PrVZtebJP6VWNYvfc94Meehx/Wp3veeot7I58XQWxTkur1kpTQ2PUKn88YLl9PEDLkOddY9iLQti9xOx/6/vOf6P3nu3XD48CHShi05jl3jYqIx8FCIeX5vHWxdV0f1TgOkFedCUoPOzS9O9zrnZcnms5v+WEHe++jp02voz6xlzVdJcw0zGHtds95XH323XSWgLcuM0LkO9Jz7hcpsGr/Gr0X7GWc2zVLIsWleHqmoAvBaXvfde+9lnZtS5f6frblOWMafLXri6QT7Xgy5SF285wLAnM8w7yM8wHj6nDp3YciUpi568M8WDBiZk/LumyUUjMTqnfPRbaTzOkzRPjy27TIdFCDo466ab62dxkuumsWrvzr4fbNHAS9hLS+dZntWtcF4UwCdXZaopJe+vgvbyipInbj/NjRPXD3mU49p2RxG9x0pJ85N3JukydRKX7k9x2U5xT1/jcA4Gt+J4ojwQzkOFfKh5x3UD9cMKMBF8+yhrl6eQlOH3q6Z4hz8eTJFjEszbCzc/zIbvq0pq5o56Z5SnmkWYOtKpNSY6Nl/rPuw/Qb6NttSsfcqx6j/TL1NOJN4m3avu1ecTcqCipsbXxG4rTwHl3l0n3f03tOxzkjzsHaprXY2PaZ/jD7dOun+Nn7P9PbyVwGmGJAhFgI25sN7510wFAjthvnbVKba/7v/mKG//2OgAeOtnr4k0EOOFgzpldl0vtOB1ubrB4z82ivNnATRFzRF6/tVWG/8uOc9exXHjE7qY/gcclfKRz+nf1MvIBccwV7GFUbWCZq48Zb9LJuQel23XWW+YIhg1E2e7bjnjAU6UUM/fILy7pd9z9geCCXPmf9AO1Zudm6TUfR/FniEpAWuqcueibAMAiF/Vu8G6rldHQC3GNBc87t9cy9MJdSs6cQac/Ib/oJWDveEG7qSOPcDw6Aqe8LwXbrFo4ejbof/tChl2KPUGtSM8ukA/vhwM84N3vOtXtEXVY82T3Xv71o70a38qLpQOKSa39HtA3E5YoX1/5sDRdkV88ivjN4ClZQOktYeyiiXEM1vdzVxduL/VqwX6P2Um5e7E1CnHftbcfiNyca74lISDB5zpXPX6umy5p5a/l2vYJRSE17shvxI3qWW/LfU8WtPx3fvVuZSKJSTWxD/ovEkXGeIwQ1JmRZCdMuKQjhl8ePRImtfrhXKZceJT08lbuFSAT1v7op4WeXB7g3qksiWHnLHDx4gbvyrgZngv7KfvYrJWzK6x0aa7R2HjyNeFMHX9IfIrJrHovGo8c+itqiWksb2e9caC9+tVPvNeBx75H3orpQCfPhkCDJHA3XO8X6ZC7rnnpREPHJlk/0dbvuf8D4PurHamGN65rWeRp2Xw4SsK9Y+Q7m8nkJ0cLaA4pg2RXoOwq7YMpBA41aoFpqRLsH7hPsQCizvrQLBidfYoxL8aRqzZYfe6x1+zb3dJT2sH+3c5/jTzO8qRmrKbp7VXLt7ca82lloevElY5EkWTznZkG5lkWLrNvbc7L9cq87gCAl/qwbJDBABx7hvam5IsVrP/Ns51XqLh0ENeLNnvO2uLtxDlirYeSKcQ4AovroKlSd/mvV8e7+Tz6B2u9d4mhvN85bChkQiaDyhBMAAFJTk2Mb9w82PWfUZ1vJjOkAgtdMTxbtnGYqrD0ux107835eyWxi95TbPekdjVAQLJx/1Rfb1f+JjfnOEtZeUlGAeT8Yh6MuGpmR/afr90nWB2HpO3PgwY+UtMe3lztTup649CC8d91hihGv9nNF9V5uiUmobW7E6J3Ke9srp92N+Se6O/YAd2fE9ttuV9a5lG/22mbLDT8PfDy5ChnnOYDISxyG6ctrXsboB0ejOWYdyZK45BlSDngr05434jxfcTBzyMhBy9xHuoMqxIZEIZBaJ+dWQ8vtJlvyq6MR3X2QZdk2yd2rxE3CVVonTub+Oec9S5UXpDkMa3eBd1i0vdOcXNimi4gGuC4IJzABV0+42jiOv/8dACB3q9YFhDaqAQl9y/oG8mJeNNqjZrHb0alh9HJAy1bKUuRQWaEyStRQU4wvfnEkThhnlC/SrqFkVKLdEEr8BzUEUw5zqL4eQnESgyAqe/71EOR9icK5je9RMmMGAKD6u98F4F5OKRkKhg5F6RFWo61pl1F7d/rJg3DC/4xHUZnpms+E5zwFHM8il4EiuaXF8JzbBkF2/PFP1sZ9ptk2zq5xzgqcAna+bPnKf3234Z6rWNyI0IHsc035Kbl74KeBkgpmz/nG/Rst686oMRSJzQZhPIXjzhSD310LADjhY+X4nrl6PBqeesqzvWskXEjQr/+2lSsDfa75/W7XuGj58stA+0gWPaw9g55zt4GXWJv13rV70rPFl6/lljdPKA+W/tV3pDL4Xdc3sRdZD2vPkQGR9tBnRHXGohvsv0+qgX7JbmaOOpU4x859ygDngTbnM3JK/2r0qS5GWBQQV58Zh2z8CoBSfej+N27Hb9//PwD+YtB2GioaPNeZn9X73n4bS4cZ7y2vsPb47j2OZRUnnhD4eHKV3HhqdXW46DBm/2+hctFv3r/Zslzikm8+j1dYe1gM+xvnAQyydI2w/2iWUcswlsD7GhYZ7Jfp+013urY1h7VrnbhExrmGyERcdLWIu0+eh53FlZ7tzMJSUlMTStqCPZRYaC9gr5kOQOaSvjzEQigOO3UHCgpL0FrA8PsTBNxyhnIOn1n5DOTWFkdbO0Wh5MtPyQHDh6Qsec6L1FqcP5073FMQxe1d8Y8LgodvhjzKqoV79wagCI/pn1VQoHhpzZ+fwLhPhcJRyih+uI9yDO01zrmac21mwHgjx733sCr0Hmr7HXIkl8v+LHLL35SbmrD3v/8FALStsnrmY1u32nZou5b9jNQOIJ3CXAAAvwHHhQ/p0+Fin0GJPWuT/lhz5Qw/gg5ymjtvWm1cje6rGxERIqgsqLR4zr/cnhnjM1lkwQhnj6hf4+jRp6BolLd3zu134WJIv/53/e3v+nJ76pgZ1wgd2y0jVKRXsEwbIG1v+s39i+7H6AdHW/Yjc1nR3wkQ5eVXa7wj2bXJmoqQ7UdpqDqYgFdppfIsKihO3C/gemmu1I+rK5AuKY6J/dz7KV6YjWhJ5jhrqiLKev70Bku7Xx4/Qp+OiIJewWjk7rUAFJ2oQlMk1UtLbO/TBHhpY23YZwxg7Xn0UetKDxvFTetHrKlxaZlfkHGeA3AWDxzWrqm1e+GXo7p091LPdUGIxxKX0kjEc/1n4JsaI08+lsD9GhEFgAe7TLmLtySRca698CNCBPuKGb7t5y+SYu40fztlKm67PZhgFAs1AYKzVBPnslFKTRBdPQGlByslJD4aIWBviXJ+n1v1HHb99V5HWzuplFKL79wVrF2WXOcHok4hOAcu94FWsq89xDaq3jrTtcZcjHO3UV4poZfcBfV7dP/p9SgcotR1ZmGlk2QWDEwFHo85jjNiSpOp6+tUvG/vgEBgEvRcQ7WK0Vd+3HEonjQJoRpnR1Patw/hHkrucUFDg2Wdo+SdLi6mXjdbrAKWHU3RuHEAgLI5x6DfI/9ObuOq/s5lPmH6QnejXnhhpc8A7Ed/Tu44AnLK4FMs1UPsHDtASefgnPt6wWsvuxSH9DkENYU1FkMupXKS7UB7hl81/ioAwJonbsaN54hoizBE1cHE7aodvKfN6fVJSNgwzkPdjMG0xie9PfCWyBGPPoJYmt782nTlnP/pSyXKxXzutcEXvyhCjVw1FLMtCCcEPd9JeEU7k+c8k6RLMC+ZUpXK51rntdxxu5Dz8B6G8VwYFiHbnqFmxwzjMlpiyUWaNUXd03HMzkV7lKpXCmDJtGnOhVlyHKUTMs5zgFC8LwTmXiPbfvPFedy1s/GHw/6AeQPn6fNXjrvS0WbbgfYpD+9sar9ysT2fOxaXMalfFWYMch/pUr5/ogeQJv7m7jn3e4BpOfpaOgBj/saHPVwmHPSZxAWUDb3ZsXgvX2UJa3c7t+XHHO1YFpfjgYbevTQI/JAl9y81uJvyMp89vDvKCkOuyp4dwa/mfwMAWO8iXqKxcEOjZb483Yq8pu/OZQltS60DX3Zjff0l38O3k6eg8T/PJPc5Wpi+6UWlTbfbUI45PeeJO1WZOue2z02Qp1p52nfQ+893o+cdv0W/hx9yHQyJ79yJSL9+AIBwLyX1oXzuXEQaGiDbc3W1nPMLXgSO/6NR5m3qZcl/lTRQceIJGPjqK+j9+9+jeEKSKrh7XCo0NK63zv/tKKV8HQChJnOK3WYuHHWh6/Kbpt/k+5xqKG8AoBhkMTmGiOAeVcAKChARI4jKUYvnPN2K8UE5Z/g5WHz+YhRX1WFZH+X6Xn6cIsh03xwh9WMLifq9XzJF0XYRXbygvf9yjzHj9qy2Lau5JHGd+WR4Y71S7jRdOefaO71NasOra5WKLEE855lUYw9Cn+GKd3PIFGuZ2WwpSne/8RcoHDsGpYcdFqi9np4QYDCB29S/CXey9fvYbda2mBq1aTse83xBWEBMTWf7sF6N8nn/XX39NV8+iUKPKk3JYnZOCSXWwSPRIw2D21LQSmbM0N/3+QwZ5x2MHHeGLbN4BaTyNzHp4UlYukvp5GujzfYHuFco1xF9j8AtM43yT5eNvQw/mvgjS5uw6B+W1Ov3v8OA551iDVqILjPV1K4oSC0E7pV+1vq9MUlGTJJ9lNoBntBzLiNc9QF2RY0cRN1zDtnV4P3snM8w/8T5KI0oDwC9c8j8rW2xzOlNLIhaz9Gjxz7qaAPu10EwwtrdPOeeHtJw4jCzlIxz9UH8s7nD8OIPZunLV2xXwvJOHN8TBw+p00vgdTTLtiqG1c79wQXRvBT+LSQxmm02luVm6yBBqL7eYZwfeO89AMDWm24K/BkAIJQq955QZryYdM95O4xzLkmIbd6M/R9+YP08D+O8ZJZ6HWRsRNp2LSXI+WaCoKiz+6nZt7VBVu8d/XyFRPf0HE1crHoAMPG7gHbfDD8+yMGnHcYYIn37pm+HC23PpA0LgK/VgaIMh/DXFCoDr1qI+znDz0lqe+2ZKHMZjyx7BFHZ/XnIQmFEhAiiUtRiEG5rzk45PM3wNr9/1swZjYYnn8Q3/ZLvev1k8k8AAJLIwOPKOeNx9T5xMfTKDjeVYjI/j7R7xrZNWq83WAfI07m/SQ9PwvXvXQ8gcVREUXma00NSYPgMRdemtNqqI5EhnbyEVJ91Fvo//nhgr6vWB924PHGUR7RVE+Ak47yj6FUZPHXR7lBZqval7OWJ+9caaXmFYeMe67NfEY4ruv2X+rKBjZs8yxsni8U4LzZspaKxY92aAwAqTzrJkpLT45bfoHDo0LQcTzYh47yDkZqdIYdROY54haLkvXintd6uZAtHTBTWbmZHizV0068GOgCUz5mDgkFO1el3/3EpxNpaiCZj7M5D3PO+3TB3htdWWMPGo5KMqMQR9hFtmT0sQah56AAK6+ejRTK8YXrHQHYPay8QCyzCFHoblviNueGIEZb5kM2O0Dw9APDrGb9OuD9mCmt3PbcugzFDqoboXhM/gngW7Gil1PpUFWNET+OaGVavDEwwMIQE5lBN7yi090syr4MgHREWYLBDx+SpjW+2lp8qHDkS8NBnMA+0RAYMQNkc73BeAKj7/vfR/afXo3zuHOM40+A511SepR07Lcu9jHNNk4Kn2zg/0hlNon5Qu3fNozG0LlTC07XfrOm5+UZqghktZFYLq9M+30PDo1ORYeP85VNexoKzFkBSB1yS9RZr7RMJu7FIGAViAVqlVsd7syMZWaN4l7RnufmZvifWiKLR3mXvbjroJs91WnTXtrad+vOFS8p/ac8eYwANwMDXX7NsG+rZE+G+fdHtWuuAPQAUDFdFl3I1/lsl5nKd7mr1T8HKhW80aEI3HHr2UEw5tj8OO3cY5l0zDoCz6kiu0tKkvLNWf5lYrf2NBxXnUqw1u4Kauc7yBdYc7Q1LU0hvUXnr2kOx7Nf+/QgN+xX3jw/WAnBWQKopNZw618werE/32a9cA8zUj1nQYwROn2wrs5mAyoJKAMDT857GzF4z9eUDKwbq0xbdFQ+ldgCI9OuHoQs+NrYLWIUg1yHjvIO4YNQFaN16PFq3fAdtO5URbc6Vl3aMG0al3VNufyFJPLhxPqn7JABGzl6quXd/+vJPQEiEYMox1rwhQWhbtdpzXUziiEsyIiHv1+ifzvQP6wxXfuJYFpfj+HDTh1i3b10g5W6tAzilfxUShe4uarKGMNtr1mreeAAoDavTfkY/i+vH4CboVzzVaYSPqh2FzafPcixPBzJXfi+7t3mgGtYucw5RYFi7qxnReMcP/58wTvFEzB3jPWhz2iRFNC2Z6LHuP/MpI6VSdozyEvRTyi8YMtjhOdcoHD1an+axWMIBAaGoCNXnn28RbExHzrnXYIVniRct3yvdxnnDTPflaTCudv3tb9j7rFK/POHAi26ci9bPzxF1+kxSWtSKXpHFmFd1o3uDVW8BX7lEAwWkMFSI4nCxbjAn+x7S2ps9sFpE0ANHme6LUAilkVIciB3IWsgwANxzxD148JgH9Ui1lY2GovoLq18AAIzvNh6AM2f6lCGnYOF57noHmlioJJoG5kzPGbdnhD7PGAa9+gpqLr7YeDapA+JimoXgAPf88PZy3bvXOZZ9vvVz/41ywDpnAsPIWb0ghgWMmNET9f2V3zub12gyhJKoX71roxJdF4/mf85vJolF0zd4EQkJFu+2H17XnOb5PmKYU5enrtRq7Nr3ccGZh1oq5gRBcxRGhIjlGTiy1hDHNJerTcZxwsLZj5ZJB2ScdxA/nPhDnDDgdEAuRHyfMlLN1BDqUOkKvZ32ItNuALu3QOJSYM/DYX0Pw7MnPIvbZt0GAPjBhB+kfPwxJuPbnYZRmkwHy+/GChLWnqhObUHd645lN35wIy59/VLEZfccfTvabzq8RxlW3modhbQ/jKK2w7Eb52779TPOC+uVVIIQC+mebnOovJsh9Z8V/8FNi+9wLD996OneBxMQSTXE7NEM43pXAgB6VhbpuUrX/8dWL7oDGFinPLQH1RkP7w+vPxwvX2MMVmgCJ9p3CNJHK548KWGbcE9lYMCv3jcTQ4AsW7zMWhm01sVGZAyPRpPz1mv7T4Nx7uUB9zLau/3wf1A4dgyKp7qIr7QHj9KPhkBb6ph/64Tq51vVtmKees57JpmXbkLkUZxYfSP6FKi/wdYl1gYPnQj8t/2592cMOwPHDzg+qfKOgPGueWfjO/oyLe/81YlW4zwiRhCX45ZB7XQ8E5OhoqACE7ob58NsnGsMrVLCLt0im7ze7z1LlGdPXDRKCeph7bDqXAiF3qX4SiZPRvHUqeh+nc3Ybaex+PWur/GjtxXP/N1f3q0vT1cpu0+3fupYlqhv0Ly3/c+RdKNnFeSJ57y2t/KeHTEreF32fK9znmkGT+qeuFEG8LrktBzzv5wzAYtvOsqyztEnsEXs1RQlP4B9ypBTAADVRdWe0Z1CkRHWzgJWEAK866HnG2ScdyC16ggUl7xzROzKpnbPuczlhC8kMwMqDbGfioKKwKVtNJrVQTMhHEZrq1G6xnxDxffsweYbbsCGy69w34nJ4zGom1XkIRqXsXZXM7Y1eSvBe3VWYk3eJWgW7TSMxiAhzdpnSFxC1GYYvLz2Zevn2kJ/BZ9BYuNcJR5JFgVRP46IrT/z/EnPO9rbBwmA9CgTxw6ZrR6P9XteNLM/XvjBTEzsV4U2tVP4ny+cZSwyTVzS0gCM4+tZWYRh9UYIvqbkrr10gkRsBiknGMTKZ5o4itl77tLxDeI5d0PQw9pT7/RqqSa1P7jKdX3PwZWW+YJBg9D/8cchlqa5RJyXcR71FvvzwyKCZUJTeq290imUiebdRk62dr9qpyuJZ21WufAVYIg6qBgx6WIc/vPE20q2cGG7eJxGvxnG9PnO51EiSsIluHXWrRa9kiBq25rHXMsznth9omuHjoXDeuSR/RmeTdxyrrX3fDIh/to2ccG4f7Wwdvs0K/LuYwglJej34D+NFDY9T6h9BtUZz5+BV9e9ivVN6/HR5o/05VIC/Yj2kExfKFfQDNeFb+RW3XMv9Io2SQjr5XiGRNap6ZX+UqtB8BLxDanOhoKQiLJC/z6JZHMKcCn5KIkLRl6AL8/9EuWRcu8+q+m5kZTn3EPVPd8g47wD0W4MHq/0bCPJErY3b9fn7cZ5XI63S332pZNfwoKzFgRuv0O1d7gooGp7C/rsUL6DOfx6xUHTsffp/2D/W2+5e/NUL13ZnGNw7VGKx0AznmLqje0n7uVtcAa7CV9c82LCNtpn3PnZnZj6iFW07s7PrPn1sVQ85wFUrs3fU7Q97/qV97PksgPuxvmWA1ucCwNSfrwifhVTj8Ou4CkIDCN7Kh1rs2H87bYUSoS1g6jEEREF30EX7foyVFHTnHPuh/pyMHuyYluc54XHYinVs06HIJyWsxru5gxj++5tM3D8Vd4CLGnFS6RyeeJ71o2yww/HsEXeZdBCdXWW+aLaNmCrKfpDzznXQn/zxHMeigBRdfA0arofD/5x4m3thqxXvr/ZiK8d4t4mSYJol/x9yd8t8yLz0OYIhfT3UpscXCwy02gh7O1FM3Ilk3EOk+e8+aOPIVZXo9uPf+woQ+RLmoxzDbunPM7T4zl3w804zwUROD+0n7llXweVpWwnmof/QGMbdqwP9q7Pk4j9rJFsCbR0kchz7sV/rzQGZqVW67PVPCgYFMaYfu96/RbmqCC3aiyekHFOJIuk3Rnc+0Z4ZuUzOOLJI7C2aS0A54tO5u7q40HR8v+CctfJar6fKKL3lijuekBCcagY9SX1ru3dlJC1+s4lU6fpL6aw+jCIqYbWkSPc9wd437xi8arA3yMRfg/LEwZay6eVlFRZ5kes834TacY5S6ACD1ijEU5scKpER2xeRrNxXjxpEqrOOhNvbXgr4ed40fO3t2PY0m/06zTkE7otmH6vo37/rme7TBCX5ITqoBHt2JPoJGhVCdqLlo9ufrlE1zjLW6Ua1o5QetTaAbjmVJdUFiSVZ9guvKoJrHg15V36DXjEthqDJCXd29Awe5fVO65FT+hh7XnkmYuUJm7jRotNjMjLODe/i1IQmnTjk61OvRA7dtEvgQkoCime4bOGnWU6PiOqzOw5d9Px6EhOHHRiu7Y/d8S5AIDGtkYAQFxk+r1v17YoGDoENRe5l6zzovqC76rbpkfhOCbHsHS3kQKXLrV2APhm1zeWeTfjvDjHjXNBfTcNGF+XoGVuoGVArfh0G5641Zla4Ea+5NN3NY4a0R3Hj+2JgXXWvk6iajbj+lTq0/FWW5RrCp5zM16aUDxVz3knCdsg47wDMUJKvH92e35aS7xFn+acK4JwaeoYBaFRvYe5yVB75ZRXPL33bkJYmijTnn//W78NNcOvLS4hKsm6pzMZmJC+0EW/AY9SW6fX7jnv3ui9Xz1sU0hsSGnHsPj8xbhq9OWO9fayaObj6PfwQ6i/0UPQKSCMMTDG9GgGvwd2rJ0P5PYQl3nCkd63lyvRJ/vaFIPi4MGJ0zm0F0C4d++Ebf06H0ZYu/+Icqph7WkppaYOorE01SdNGa+w9rh3mkt7aP7oY+fC/S6ltvLROB8xz5g++2ngVKvH2RG+DgA7VwLv/z7Y/s3nJE3vIHtFETc0YVP9MOS4bpT1LTfKf4nV1YbnXGrD5PrJOGPoGbhinEe6VQdhfl/rAqEJCAkhdC9W8lKvm3wdFp+/GJPqld+BhUJ6KbXmT63GEm9J/r4pO+wwDF+2FKGqqsSNA2AfRE5nWPu2A9Z71W3gxVxDOlSQm160guIQSiryQ1XanhsvBRGBJdvclwN7sxPZUxgWcfeZ49Gjwpr2kqg/ZSZu95y38/72TLU1OfqS8px3Esg470CM0hkM3Md7bsYc1q6NQKcjrzgoknqF7I7v1Zf5eSLcPOdlhx8GAOh27Y/0Ua2w6vk80Kbc2AUpGOdcdh6HPfQ7KH6jbfaRvbjtObHUp4qE4TkPbpwDQGzLVsd6u5cgnuAyuGbCNQk/0w3dc+7zwH596XbPdZkmFmAwp7HF+L3f/fFh+N9TRvu0VhBKStD9hhvQ95//TOp4zPU4AbiGtZtZc8qpaFu5EpDl7IW1q8eW9ZeeV9i4myGZBuQW02CnNvGUi6dRM87zJawdAMaYhM8GzwZGnWJd/+ta4JP7rTGnf57o3E/EI7Jqu8lrmaZ30PSe0xO2uWq8VRchKkctZcqEciX3SohE9GdkW7wNxaFi3DDtBpSZc/CzzO0H3w7A9F7w8BotOGsBXjrlJcuy6sJqjO82HkWFpYCqNyFWVVratHz1VXoPOAm037lNsnbe0xnW3ipZBx9G1TrL0uWD44xzYN/uzAxAphu7cR6PJTbOIymIhHUlYm3WvoHgU60oE9jvkWS8zWn3nHuFtZv6NyySR+/hNEHGeQdifcYF++ndSpJk3DhXjYtfnyEgFlZunJ3R3frqcNxnWNTFINlwqaL0K1ZVGZ5zNXz0gOrZTMU4d6sZP62HU01aqzvrh99vau9AxWwPUr9xFt2g9vGcixIH58zykNr+299670s/MO8PntN/TtKqyBpxPazde/+Du6UYQpsGEqn7A6awdgB9a4p19fZEVJ97DiK9fcqC6OmZxm/T8NSTliZGWLsq2mTzsrd+/TU2Xn2N0jYVz3kksXG+629/w65//NNzvRHWnmXPUsjDe5QhQa9uZoVqP++OZpx34EBouwnizX7xWmCPM8UCADD2TOV/EE2TdnrOtXq24+rGJWxbYL9GuHH/iYKIhn8/jH6PPALAeEa2SW3t0mbJFGv3rgUAXDnuSpwy+BTPkPeIGHEdBA8JIUiMQ25WBBPF8vSXQUsVc9SCmXR6zp9b9Zxl/qJRznecWSk8Vw31aEscaxftzPla55tXNGLJe1bRV8nHOB8yVYn2GDbNO02RAApMgxeV3YtxxPnDO/Tzx/dNPUpGbrW9m9t5f3sNnvKoqX+TbSdCFsi9t1cnRjJ30gN6zs0vNs1Qz3RY+8AXnsedJwtY3N+4PCTTC6/ljbc9t/XyFgKwdOg0w+9AVPlOyYa1S63dEd15hGN5UbgI/Sv6Y0q9URu8Z2niEiB+HTn7yJ7dONfE204dcip+e7DVqNY9JGoIvtRqHEtICGHgZo5HfythjL2/rF4roZ5GLe8gysZXT7gaALBlf+rCcHFJ85x7/yYrtu9Pef/t5YnPNmKrj7o/kPz1FJRQTTUAQKwxQrEKBgywtHGotbuULdNqC7cnrH3LT3+KjVe5l0fcfsed2H777Z7l1oyw9mx7zj0iBxpmuS9PgfJ5hn5D6cwZPi1NFFYq//PJcw4AJXXAYTf4t9ngkec9SY0g0AYm3r0TuMnD+GtnuL+mPB7EY2M3UmUu6+lfIRZCweDBKJ4w3tK2TWrr0AizoGhaLRUFFbhp+k0oDHmXPHMjxEIYsqQR0bVrAbQzeibNaL93W9xmnKepzjkAvL/pfcu82/VjXhZrzZxSfDoIFCKeRZ656wu0HbCV8/U4Zi5zfLtgG4rKwlRKLQF9RlTr02f/ahqGTO7YwYyrjxic8rYtH1tTw3Y98Ld2HcuFo9w1Miye83SJ9eYRGTXOGWPHMMaWM8ZWMsaud1n/Q8bYN4yxRYyxNxhj/TJ5PNnGOkqavOdcE0P5eufX6TwsB5GGBnwy1Hp8kml2yy9/6bltovJOuiCc6tncr3rOIz7iY24IkZ2Q486OY4iFIDLRMnofxKhNhjisL/yQOnv+iPMxp/8cyzqtw1LQXRnxj+83VI6HVA3B8A3KNTFutXUEXShVPNPFE4ywUzfxm0+GMHx0kvGg3dWiiCd9teOrwN/HzlcbFIEor7Ib+UCmjPO2GDCaAABW30lEQVTq889Hz9tvQ8UJ87wbaZ5zzTh3G7DSwspTCGsXCgxP4r7XXsOma70Vufc89ZT7Cu3zs+059xporBuWto8o8arN7nd5n/M0MPdOoKgybcfRIfx4JXDIdf5t5l/jvlw7F9qA8Ju/9t5HB4qs2Z/fsqkspf2ZqM1LXMpJz3ldcftEwJrjRolBLkm5ZZyr14+9Yki66pwHJQdPuyd+XuhcJdoSRywqYfHbG8E5x7KPt2Dzij147DfKoF++qNBnk2yLliUSgPPjwJ//aJmP70isG+KHXUtJw2KcJ1HnvLOQsccYY0wEcA+AOQBGADiTMTbC1uxLAJM452MAPAXAGcvbiZBT8Jxv2GfUwnxhzQsAgA83f5jW43Lj2ROexcmDT9bnZdOVwpt9ahAnEMEa0l0JYTlriiLko4W1JzKmfn/o7/GXI/5iWiIC3LlNWAyDMWZR623vg/Dx5Y9j6wEjB3x3tNGyXiul5uap0TosTFB/F9nwlPxk8k/16eM/jaNtteE+rzxVyRetudgI23Mzzu88RcSiowzPbWVBJQDg0D6H+nwjb9bvasb97ynH0dicvy/Z4gypjbNQCBUnnAAmCBj6+WcY/J5TqV73nGth7S6e85aFSrmv+Lbkc/dZodXb1vS8d81p+cAB1+W6kryLWnuHc+Jflf/dTOknafS2eeWrNe+wdQoqDHExVPcHplyStmPIKgd93zpvEhm1oD1fguT7J0grSURS+im2x7c5TcRugJu97LlonLdXOX7hDqNMYMuiRbllnKvn8ucf/NwSqrpk55KU99kc8+lreDBgXH6ooAPA1lV7EzfKMT6ZvwYfPbMK7z72LdYs3Ik3/rkUz9z1JXZvdn/XEO4cdNJAHHmR3STKXfbMnN2hn0ee88wxBcBKzvlqznkUwGMALDWpOOdvcc61p+/HABLLJOcJD7y3GmfeZw3/sOomBPvpH/zmQX36qW8VL1hHjLoNqByAid0Nr21xq2lgwScU1h7WbhGIY0Cf6mKs+d+5OGG8Et69v1XLOffvpM3uNxuzeptCXbmHcS6EsWLPCkspl/b+Xhv2bcBFr3jnb2uec7d0A3u+ulnELiIUWcZoVv/Pj7C5Ue04a+HwJi+pV8fOLJKjfdfBlamFLR18h1GKbXgPbyGlXpWG2md5YQ4YeDZqSjKvhCuUlOh1sxtUD3XDY4/qLxJZDSk3l1Szc+D99z3XeZGMt5t5eaalHFFrB4BxZwI37QWu+BD4HzUqKI15qoFD98eekbbPzCkmXhCsnWac+3k6J1+snKt28tMpP0VDeUOglCM7HByT6ycDcJbpMg9g5pJx/vIpL+OMoWdgRE36OuO8tRU8FoNQZjynK087LW37Txbzb68pzQPAR1s+Snmfy/cs91z3s6k/c10+9og+HZ7Dmyov/GVRtg8haaKtcbTsU95tL/11cZaPJn+ZcHS/Dg9nbw9Ns47M2L7/M+8/eOzYxwAozoztv/8DYluMCJysp99lgUy+vXoB2GCa36gu8+IiAC/5rM8rtuxtxaKNjZZl5hF/bjMsqzDBc18PLH4A65vW6/PnDD8nPQeZgOHVxguum6k/Vn22UVvWLnZlV2vf/dDD+nThcGV/jDE9jH2/qlqZbBgyE1vgdvm2uHiFhDRc5pv2G6Ioss3W13LO3TqD9mV9K4xc5bgExA8YXu9l2/dj+m1vAgB4VAnLZ2Ej7NnNc24naE3Zt5dvNwYCPPALfXrnx4fi85/PxumT+qA4knsPzp/MSV9YdBCKRo3E8GVLUTRuHFhEGRhofOxxZaWPoZlKWHsy4ihexjfPlbB2O5onNZ2ec9uoe9HYsR4t8zeNw5eqhmDttME/OQ5EPbxgaQpnn95rOuafNN9RdssNu3edc476YqVTmy/Gea/SXrhh2g1pPab1F1yoGueGOGf5nGPStv9kMZ+nlY0rMaNnQH0HHx765iHX5cOqh+HMYWe6rmOM5U0N8Xxk2EE9HAruRP4xqV9yonDxvg2uy0sObr8+zOCqwRhZq0TOtS5ejF333ovmBQv09eQ5zxKMsXMATAJwh8f67zHGPmOMfbajnfkNHUVIYLrqtYbEOfrVFOOPZ4yD/afvzrxfZH/84o+4+NWLMaBCMeROGXyKZ9t0MrhqMD45W8kjMtfUthjk9pBdm+dc2m2ovDNTKKSWc/70FxsBpKbWfsQw56jjhqYNlvnySDl+OOmHSe/bjlnUpi5mDSvWjHM349neGSsUjTz5D1fuhGApB2cYw1tu+LmyfYlR1iiIca6F8yfq9H73H5/i2D+959vGzzgPiQJqSgsQEllWa557UVWcvYc5K1B++z2PPAL5wAHdEK69wllvOb5zZ/L7TyYSxMNz3vq14qH2FXDMBva853RgG8zo9hMlJ7t+UqO1XcCBrbwjaOqC+bdf+4F7mwX/l55jSoK+ZX1x6ZhLcd1k5bzF5bj+XLUb5+booudXe6d75DPPHGTc/zwWg1hiGOdFE11K43UQ9sgxbb5vWV+35oGIeaRY3DjtRt/tBJ9KI0T7YMxajZHIT6b0r07cyExNLbYWV4EdcjjkUAgvNkxDuKEBQpFH6c0UMUeL6svCuecAyjSZNM43ATBXgO6tLrPAGJsN4AYA8zjnbfb1AMA5v49zPolzPqmuLj9GRAWBOQS1ZA6IjEFgzJFzLiYwvLYc2ILVe1dDZCK6l3T3bZtOikJFuHzs5ZBMh8tjMbQuW4Ydf77HUjcYcK9z7kbYJgCXioCXW614s1gQoNSVrS2qdbQLwo0HOTsAcTmOoSus39nPc24Pazfncc9ftBlHLTMiImQXo0soMsLHvcLazcs18Z0guY17bDnlr3+zzTIfRDQkLAodapxrA0MjepT7tktWYDCdmAXbpAMH9AErsbLS2bYwObXmZGl6yT0YafsddwIAWr9Z6ro+a+ih1ZnznBdPmICBVw1H5UBbPmtX73Fqv/3+bcAj38nusZhgjOH747+vP8dX7V3laZzblcI7I6+PM55tPBYFKyhAuJcSlJhND5NduC/EQmgobwgcyr/1wFbMfnK2ZYC9sa3Rta1bfXMzQhaf/0GYe/loAB1f3zodcJmT57wTICV5DkMCQ0uoEJIkATJHU6QELBIJ3OcPjIueSZDn2uCPPsTg9/0dTvlEJp9gnwIYzBjrzxiLADgDgKVIJWNsPIB7oRjmySsj5TAhgTkuflnmYEwzeqw/vcgEXDX+qoT7TWdZkqD8e+m/IZq17GIxrDnlVOz885+x/623LG21G1Vua8OKww/H/g/cc2rDtpHtZD3nP5v6M3y6ttG5XyGMU4ecqs9HhBTChlVKQiWOZQdiByDYbFFRyzl3ETeyezm3NhmGvcQ5ejUZ4aPcrnwE6yiil+fcbIhrn1cUKnJtCzhTETQu/tdnlvlEtcQBZVAlJnXci1q7p44Z5Z+rFcpi54wVGAY3b2sDVwcv3ELYQ3WpDRwFpeWLL1yXawMFoe7dMvr5SaMNcLXzOVd7lSGC5qb0Ghk51VkDubN6zoOiPV++eda7zSE/6ZhjcWFfdJ8+rRnn9vehWc28M3L60NPBTI/bA+++h9YlSzBg/nMY+NqrWVWBtr+fREGEyET9HH2751vf7V9Y/QK2NW/Dk98+qS/Tqo7YDf9E31PI8VJe/cfWgQkMw6cnr7mQbWSZxjE7A8ka5xv3tEBmDN9uagTjMmQwsHAYPJ5eUUrm1u8MkMoXqqpCqDaz/amOJGM9WM55HMD3AbwCYCmAJzjnXzPGbmaMaXWI7gBQCuBJxthXjLHnPHaXdwiMQeZWQ0iSOUSBYf3uZtjzGyNCIcoj/t7AbNEUbUL3RmOeR6Pu5aEAfXl82zbEN29Bm4dnzv5yPdAWrDP+wZkf4Ol5T+PMYWeiR4XT6xgWwjhuwHHGfDvqFLttG5fjeHWiTSHYxzh3etON8y5LiY/N/Dt5Gefmz71k9CU4f8T5lgEKO0EfykH6NyEhtbD2aFzGfe+uQjTJOq9aqkgoh8MWtbB2AJCamnQvsFv+d/X553fYcZmpvfwyAEDJNI8yY9kiTWHt5trzrqPusluHogv3OCd+1zDON3/p3a56YIccjhtmQ1x75tkHGv0GJTsD3xnyHWyvcomwKi5GpE8fly06DnvJtBALQRAESLKEl9a8hFOeOwVvrHvDc3uu3n/f7PrGERExrts4fbok7Bw0z0eKyyOQczAlLBFc6dhm+zCIdpJs8MPelhgkJmBfcxsY5+CMQQiHgTRXjHCrbkM552mGc/4i53wI53wg5/wWddmNnPPn1OnZnPPunPNx6p9P8eD8QgsJNhtCMucQGMPmxhYwwVDYjjWNRLU4rN1lVjLF0Q1HW+abnpuvT4fqFM9b+fHHAzCUqZNVVxzRM9jARHmkHEOqlFrh1SUunkghZPkd1+5dm9RxmHE7HzE5hu228uoNRUpIoZsx7xDMk4z8wG5FPZI6Hq967eZcv9JIKa6dfK1vzrlZC0H2eUIH8cKERQFxmXt6473494J1uPXFZfjb+2sSNzahDQRkM2w9Eeaw9i2/+IWR1+2m5p+tl45WDaCTCsKZf1fX39gtl7Wres5v2gsc/0dAe375/fah1COR2otkGrDRnk12z3muvkPThfb99h2TY4NqUN6NZkRBRIiFIHMZb294GwCwpsn7eb+zRdHfWLB1Aa5/93qLgW4ecB9V4x/Sni+IIQY5nn9GrixzRFtzTKuESJqrDh+UVHtRYJCZgGG71wEA5q1+HywUAo+lOazdJUxeSkGbJ9/J3R5unqMb59xpnAuMgYlKblwkNgytm85FWBRySl3WzNnDz/Zcp6mKa6P2+99S1cZtN1jNpZf6fkYqAl5uOdEhIWQxTCd1nxR4f9WFVoEMN091m9QGyXaajul1BO445A4UiE4hC/sov9zaC7XLDkPvL+biw1W7LOvCNs9D4Zgx1vU2418T2rHntSfCPGAUcxmlTIW3v01OqLElprzcG1uiCVpa2bpXGdQK5XDYojkVoe2bpXo0CRMF9PjNr22NM/s9wn3dxZj0AYNcM861AYzXb2rffkyDg65iMm7lwsZ1TBWMnCWAcnqgNhmittgIWbx49MWY0G0Cjh1wrKVNrr5D04VmnJe9rJRpZUVFKD/2WL9NOgy751zmMgQmIM7jeHHNiwCAbQe2uW0KAPh82+f69EtrX7IMxphzzO26Ml4MGFeH2Rfkbh1pQRQg5annfPOKRs/1+ZhH3xWpKong5PG9cMSwYKltYVGAxASUxZTUzLJYCxAOpT3nXG5z6obENm9xadm56dxvsizi7jlXlpuNyvK4Uq9VEFggNe5sMLRqKHaXuq/jai1nVqSEmO9+8F/KcluoS9Vp/gJDqeTKiYKA1i0nWpb1Ku1l8Z6URbxrddv542F/xC0zb9Hn3Tp6USlqNc4ZQxkKcUyDewmbumKngOFfnnkNv3vFmcHR0LRV+YwNiiBO6yL/GqipeonMnnPt+oyn2EnY26Kc5wv+8WlS+xA1z1eS+epH/v5dAEA4gEbB2tuOxdrbOr7jalcb1XLOIYZQeao13aDINgCTLsQqpUxKbP1696gGLdQ+gK5Ah+KSGpLSbkIJPOduxnm3ji2/l3MUeDzkzbgMQHYUs/vO1qfrS+rx4JwHUVFgDWPqV95Pn/7FtF902LF1FPY+Am9p8SyX2NHE5TgO6X2IPv/imhchCqLFA95Q0eC5/bLdy6z748Y9avfKB2HOZaMxdGru1pHOV8/5u4/5awdMOyF7qS9Ecvzu9HH423cnB2obFhXPuRkWCqfdOOdR571ecfJJaf2MfCDHemadB934sBlCArPm8mpe3pDAcHjfwzv0GIPCGMOvzxTx1AwXdfRWZZRLKLDmf9uN82RqMwdFZEBsr1Ef/q5D7sK5I861iMAJSRgf47qNw7yBRmaFm/Ebla3GOYtE9AGKZImEBGypMD4jIschMEA+4F5jeOO+jZZ5LaQzWSPdfE1qhno0RePcPNA06IaX8OrXWwNtpx1Bqo7jXPacC/ZSIJLy8mIuofhCSWbyJ815Wzt+/weX9AptwCA3OvY6HqXfksUS1u727Fnw17R8TqejuMZ/fTs0PNqLyEQc0vsQ3HPEPZ5tzIOhh/U5rCMOq0NxHcAPWiovw8R5HJUFlZZlIhMtHvDbPrkt8P7M27XGjTTAZFOocpV89Zz7MXJWT4w/MvXSeUTucujQbpDsxnk47OzrtxMtGtfyOS5iup0dMs4zhGa0mKOGZc4hCAzVJUbnPaJ2dgTGUBIuwVfnftWRhxkIzjk21TI8cbCz48zVEJTY5s3W5bY8FLfc1t+fPrZdxyUKDOAR/HTUg3jrtLdwVMNREJhgCf/2ytMOgpvXPSbFIJm+imKcB384nTS+lz59zMh6hGUJ35h0fEoKQpD27HHd1i52dOyAY3F4n8Nx5bgrA38+AMRNF2Vc9VwnK8ymYU8t+N5Dn3u0tKL1r4QUrfNc7p/ZXyS6oZwmw9NM9QUXAIw5O6wmwcZd992Hnf9nq0+tec5zzThPU5i/OZQ9Wf2LLk3bPv/1oex5zhlj+PMRf8bBvQ8O3L6z4Wac58o9HJfjluM7bsBxEJiQcoWZ333+O306IkZw2djL2n2MuYTiOe9cxjnL4UFzon001JRYPOd3jT9dyTlPo1o7lyTdpjAjkHFOpAvNaDEbQlrO+SkTDQNN83pqnkBREDG4anAHHmli3HKpNbRRrpIZ063L7aNpLh2Ik8b3BgDMSVAWywut81Vd0NNSy9zcQWhPDqLb945KUcTVXbJIJGnP+e2nGGHM0biMkARsqmWWZesvuNB1W7txXhYpwx8P/2PSde+tnnPl+jwQTa0DlbJxrfrOXw7oaU/X53YEDmNQyznPQPipUFKijFTYqidw23zr4iXW9fEczTlPE5ZQdpdSaoQHUoJnWR4JrrlVz8h3XKOkciis3fzuLY+UI8RCKRvn5pJqY+rGYEr9FADGuyPfEUQBUh6GtftBxnnnJSQwDG7coM9vKakBKyhIyjnlR/MXX2LZyFHY/8EHjnXkOSfShuAiCCfJHCJjKAwbL1PNc272QBaJuVUORvTx+MnNSl3ZULfuEEpLddX2lkULLe28vFeLbzoKd585PqXj0oQsBnWz5kqaveV+x54Iu9c9JscQlaPgAkP88rPQ8NSTinHuMtLnRcSUK30gGkdYEhEXgPdGKOe/zWck3T5YkGpUQNyU561NN7elljeUakdJuy3W7VKun8c/XY+5f3wv8PY5bJtbKDv6aJNau3LuC8cqAzShOqceQVBYcbGyy0Llmojv2o2lw4Zjz2OPKQ3spQ7t6R25mnNuph3hEdaw9twwXrKKV7h6ZT/35V7kSAh1EDqjOJxm/K461cgTZTlyTuzG+SG9D2mX51xD0w7QhE87T1g7U8qSdSJyvb48kTqCwHQxOACQBAFNL76I2Pr1adn/urPOAgDsf+ttxzoyzom0EfIQhBMEIx8dMHLO3ZTH84Htd9wJQAkjDdXW6vm122+73dLOK/SurDCMUIplsU6f3AcLbzzKYZwXhoz89/Z4T+yGfWu8FW2SYoiL55yCwiFDICThOa8qqLLMv7diJwQJiIvA7jJACkd8DRL78aQqIGjXQQCAZtVz/usTFVXcni415N1IVz/pJ08vxjdbmgJ3vHLZc25m3yuvmNTalfPX/Sc/AQD0e/TRlPc76JWXMWD+c7oRGl2zGgCw57HHATg957H16yzzXJJz32uulTtb/BRwUwVgGrVPBIsYA1m+Ye2hYNd53vPjVcBFr1mXnfesc5mdU/5mnc8jz3lnNs6fYEb6kLx/f7YOx4LEJcuA8dhuYxVBuHZWBNEi5LTvXthJ7llBYEkLouY65DnvOkhMdDoB0gBvbXUsI+OcSBtugnCyrIS1iwKDHFU8GWEhv4zz+pt/5bqchUKIrl2Lphdfct8wA3mfjDFUuJRgM7+829NBsxv2b214CzHVYNC82CwSAY8FM84dx8I5QpKEuKgY6IIUx8TtywMfj71MW1DMqRZa3fADUWVQpbxQOU9lhcE64X510v3wMsL9hOl27jciFHLZ4WvHXraseMIEDF+2FJHevXy28idUV4eCwYN1Zfj1370AgCrQ4hLmHt9t0zGQ4rmfi60pqj/7feX/9qWBN9UiCgAP47z7aGDosXoEQaeHMavmwby7gQGHAmU+KTHhEmCgTaQ0i4JwhPIOYGAWUdK9//1v1o7HTEyOISSEdMX2knAJ3t/0PpbsWpJgS3/e2fAOACW0/fKxl1sqquQzgsgg57EgXMOYWseyfHovE+2jJN6K2iuuAOB0BrQHqbHRscxeAacrQLdShnAThNvfFkdc5hZDXDO43Ixzs3J4rlA6Y4brcnMHOLbVmUecK6I1ySAKIh477jF9fn90P6KyYohrivAsEoEc0HNeU2QNLQ3LcYS5jLgISAIDk2WEfIwFu+c81YEHt1JqLarnvKGmBL84bgT+fkGw8hqp4uUgb415d1YeXWCET324cpdnu1xj/7tKuH4m7gFBDW83FjDrQ0eleOJEyzyPS7l/T2rlk+JqKF1p8DQAVmjyrrmVUuOSYrBqn3HoT1M8yDzC/LyYcF7i9jdsBoqqgCmXGstytNynmdKwEknVWXKTzTCmlFyVcqznxjnXw9p/d+jv8O7p7ya9D3tkmcaQqiEAlPfdFeOusOjL5DNMyO+w9kiR8/3RGUUYCXdKo816CeVUKxYFhTznRNpYt1vJpX1xyRZ92bKt+/DJmt0QGQML7wYAMKjGuctDbXK900A6fsDxmTjc4Lh1dAFLLuK6s852rs/DIdWwEMbImpH6fFyOI6oKJmmK8MkIwt08/WbLfFFc2S4uMsTV99yhG7/U19f96IeW9nbPear59Jacc7VzoAnClRSIuGhmf/SqDKZ7MKDOmlIwb2zPQNt5dUnaYt6DE+Z8tkOGpp6v3RGItUYHcte99yoTmbgHbAZ268JFute88rTT9OXxnTst7bgkZSSaJa007055U0tYu9sgBJetnuTpV6X8WXlDKik+jAFzf2vM54Hn/IGjH8DpQ09HWdhZbaMzEBJCqG0y5itOPDFrxwIAe9v2ojmuas8IIUTECKoK3Q1tP1olZzgrAJw0qHPWOBZEAXIeh7WHTNpJBcXKu4TC2rsOX9UN1tPq2lNOLbZpEzZf7z84zrzsjk5M/llMecKX65Uw0v9+uQm3vrjUUqpKEBgYU40irhjvouh8qJnVue854h4sOm8Rbp11ayYP25NbZ96Kc4af4xkKa34o28uqAfk5oqoZw+a66ZpxrmkFSE1NaP7oY9doATuVhZWQ9u7V5wvjSpi2FtYOAPXNhke49pJLLNvbPeWp5tNb1NrVzkGrapybxQqDcOaUPrhmtlFdQAroCZBNrvM9B4zBja+3NLk1BwCM6V0BAPjNiaNw3JhggwDZYsj7TnG7TISRu4lB7XlCUTk+8PHH+rKWL76wtOHxWO56zntNUv6/+Wvrcim4aKElrN3te+5Zq5QNm3SRMh8udrbpbFT0Dt728J+7L8+DnPORNSPx82k/z8t3ThBa4i34aqBJQHbsGJ/WmWfmYzMx7ZFpAJw6KDWFHkKENmQuoyXe4rquKJxbArnpQglr54jHJERbUxNkzSahsNEfGThBEecl47xz8+yAmfq0xAQwtRIKjxvX7/5338WBTz4JvM8tN9/smZpTc8klGLZoYad9lvtBxnmG0IzxZVv34b53V+Ped1a5tmvco3gALZ5zddKszj2u27isXqDHDzweP5nyE2915zz0jCdC80z/7lCl3mpDRQNiahisZrBHVynndc+jj7nswbY/JkI2KbsXa8a5YBjnb/WeAMDpNQecHZ1UjfO4Syk1Ldc7kqQ4H2MM0wcaXmK/nHHLMZg8Bre/vEyf/tMbKzy30Qz/4T3KkzrGbFGlqo9qZEIZPb59m2NZ6zIlN1vavRvFU6d6bCjlTAkmB3HVgxa3VUGQg3dgEw6ExFuBVW8Cx94F/HxH/sj/t4fiauAXu4BfNiZuWz3QfXkeeM67AgfMKZg5FAFjL/VWV+yMcDoQO+BYpgmtukULagPhnQ3NOH/klwtw/zXJpwFkm1DEeJ9pUW2dsBtImHh2oGGcy4zp71keM97NG753Kdafd37gfTKPCNCev70ddVd9v0uGtANknGcMu5FSXGB9gbZuVfLJl6xTLryV252Kq/aaobmAp5q2z1M50tCQmYPJMNrgiNbBkGRJ70Q4OgwB+vYhIWQJ+R+wV4kwOGn4d3DVlP8BAITVnPOy2bMd2x/dcDQuGnURqgurAaTJc65Ox9XrNRXlfPMmsYDGecyUF71eTQEBgMZm7/AozaAPu0SZ5CKcW38L3k7VYjfiO52593ufehoAIJSWou8D91vWta1ZAzkaBZckfdQ791DPr+330/PD00WfaYpRHupCL38x5D8QcfyfgKr+wHCP9Kk8yDnvCsRDxjnMpfvY7jl3e0fFXQbZNIP9qH5H4aE5D1nWFYqdQ53dzvZ1+7Bvdyv27XYP5882iSqniKaysNojhTznnZu46X6uKikAC6v3e7wdYe0ukbYAUDFvXpc1zAEyzjNG2GasaqWppjQohlVsz3TsW3obtFPw8tfOsOhUS2VlErHMmccX6tEDYrnP4EEWwmd/Nf1XuH3W7Ykb+qCFkWsdjDg35ZynEN4pMhGxjRv1+csW/xcAMLx+NMrLFO9zn33blc92eSgVh4txzcRrdMGjlHPOTUaiZvBqRnooJcPX2Obt5Tvwzw/WJNwiFreG1ms57o3N3vn72nGH8mR4PlRjFS7ibW0eLduBT2dIKCgAC4dRNHEiiqdORXzPHqyeMxdbb75ZUWvP1bB2LVR/+YvW5VKKHYAvHnIuEwuAvh5RBV2ZiecDV3/l7SEnz3nOka2qCztbdjoMOHMpNcD9HRVzGWTb1qxEAJVGSh2l0jqr53zPFmsEQawtdcVrWeb48tX1iEXTp5q9e4szwuH4q8bq06GI6dyq1jkZ552buOl+jociack5Lxo7NnGjLkh+9HLzkO8fPsgyzxhQVhjCiJ7uRmyx6UHHVGNHMwqD5m11BCwUglhRYVk24L/PuHb0hRKl1FcmwnkTcfLgkzF3wNy07EszxONyHFE5iogQSSrFQDuf9pzxspiSY8fCYQhqqYij1yu5OkFKR6Sq1u5W51w3zlN4udp/ipvmf5Nwm/dW7NCnm2NxDKtXBn2OGdXDc5tYnnnOhVJrqTtzXlba8HFuhOqUiA8WCoHH45BVvYPmTz7NbbX2qgb35cmUPVv2gjH93Pet6zgHpLauU+M8neRBzjmRfh78+kGc9KwhzLZ893Ic9sRh+M+K/1ja2R0Ki3YscuzL7jnf2bITZzx/BgCgLFxm8ZR/cnbw3NV850Bj6oO3Kz7dhg//sxKfzk88MB4Yl3dL/YAK/X0fLjDeH7u3KJGfe7Y2OzciOg2SyXMeEgUjrL0dfRuhqHNqSrQXMs4zRCRk/Wl/8NhX2NcadxgWtaXKqPBhQ7s59hESQnj2hGfxzAnPZO5AU0C2j5KpxnfFCdbSb7qKbA7lxKWCNvofl+OISTH3kXwfITS9XJ7HqDYLh8EKrIaCn3GuDQykGllhzjnXwsv1sPYOGkhZYUrjWLKpCTH1mMyDVACwfV8rfvfat4hJsuE5TyH0PivYQvyLRo9O/2f4hR6qxnfzggVo+fxzyC3GYFBOq7WfcI/78mTC2udf471O88CTFzg4p/4D6DHWqnBP5AR7n302459x52d3YmXjSn1+RaOiDbJgywJLuyDRXDFbBMzGfUY0WVGoyOI5N4vidnYEU99w4/I9SdVAj6t9i1aftLBk2b7OEGftObgSJ/5wPCJFId1mN+ecb1reCABY9uEWEJ2XqQMNR2FIZHofoj3GuVaOjbCSJ73c/MPeZ9YE4kSb8fObE0cBAC47xF2AZ0DlgJTKkmQSu9dYKFXCrGsuu8zaThWcctRizjM0I1grpeZmnO+67z7P7fUOi0fJNRYOo2XhQuuyTHrOTWJskj2sPUthadrggF3t/ZEF6/GnN1bg/L9/onvOs3WMyVI8xSpuJFZWpv0zKuZ5l1asu/oHlvlN1yi6BkwQcjusPVLivjyZsPYD263zr9+kqLMDitccUELbiWCMOhm49N2uIZyXZ9RcfFGHfZYWkq5Fg7227jXLevuA8byBxoD9L6b9AgDQ2NaI6965DntalYo2zTHD2xoRI502x9zO4MndLfNNu5Tc8x3r9+HZ33+JD//jLiLsRzrvzi0rjcoyA8bVodcQtR+qvqIFl0HyqfMGpPEIiFzjrxfP0KdDAjPU2mOJjXMuy9hy002IbthgWS6Y+rqh+noAzr5LV4SM8wzBPeJN7YbFMaN6YO1tx6Kh1uiQaqFg9hHmnME28qAZ60Kh9aXa+ORTSvMW9xIp+YKWR3fjhzfivU3vJZ1v/ofD/oBpPaYhHFUM0OoLL1T22115ObNwGAWDB1u2ccs59zquZHFTa49LHAKz1hIPSnlh+z2QujCdTTRt3S6l4/bhql264Z5aXnzHkxFPuY1wb+/yWMUTJljmo+vW6dM8l9XavUhCrd3B+78H3rtLmY6rg2QhMs6J/GNK/RTLfLhXrw77bK1Pog0Mx7n1nrQb58cNOE6f1oRMH1/+OF5a+xIOfvxgANBrpANAYajQkXPeWTn8vGGW+eUfKV7nDUt3AwA2LtsTeF96lyxDg2ejD3O+Z8x9haoeSv+1/9haRzui8yCa+viCwHRBOB5AEK55wQI0PvY4Vh15lGU5N0WI9Lz9dgz96kuHo68rQsZ5BxPEsLht1m0AgGE1wxK0zA6eqtO2kDa5WXnpmmt75yNlEUMEb8uBLRbPed9//F2fbnrlVdftZ/aaifuPuh9oVZXe+/YFABSOHAlAMc5LZxojknKtM8XBjXTknMdNOeephosP6laKBy+0dhg/WbM7qX20qGF5ds+5+W554jNlxDVfBOHMDHrj9YzsNxU107YVK8CluGuN9JxG8hYLDATnSnm2ncuVeQprJ/IQuwJ6kCirdBFNcA+GmfWeOqjnQVh03iIsPn+xPqi9q9VaYaIpaoRPR8SIpYRsZyYUtp7HthZloOOjZxSP+a5N+9EWNExds87TaJvLaqRaWU2h66A95xwX3jETF/x2pv75XbEedVdDvOASPD3oEMQk2RCjVMPa/YTh1l9woT4tNTXpHnQuGQN8QmkJhMJCuo5AxnnGmNDXPRRd85z/4IjB+MvZE1zbzB0wF4vPX5wz5dPseCmzizYBLEEVjtPC3vMVuzfA3HkonjRJn9509dXg0SiaXnzRtQyJptat/R68VQljY+EwxMpKSAMV7zlPEG6sCwamQa1dMpVSa0+4+CFDrPVsT7v3I99SLGWF1t908aa96nHYtjEd0pfrGwHkjyCcmUx5t5ggYNA77/i3cRNcyWVBOI0ymzhg1Kke7EdFQzMKq0zGhBwHXrsR+MccZb6LGAFE58KRVlbYcZ7mqKzcTztbdrquD7sMeGnHqymy72oxjPO4HMe+6D59vkAs6LId8zULnb/pKw98HWhbWX2PC2n87fR9evQLOAeKyiIoLo+gfqDS1ysozrMBXyJpCi6+FA+MOh7VJQW6cb7u3PPAOUfjU08F2sfqE080POimfPVcKguZbcg4zxCFYfeOr5Zz/sMjh2DuaG9l6lxGV1cMW28koaQE/Z9R1FtDPXugxy9vVFfk92Vm91RouXKAYlhXnXWmPr/z3vuw6Yc/wv433nDsRxPkEkqUHPzmL79UVqgPONakeBDEbcFEVVKtc242gLU87saWGJrTWIYFANri3oI2BSH3a6KxJabrMwDunY28EYTrIIQEgiq1l37PsUwRhMth47z7KKCnbfCy1RSBs/R5IOqvDNxzWiP6H23q8K55B1hhim6hsHYiD8kFz/lvP/2t63q/smf9K/oDsIq87WzZida4Uee7q3jNNRIZsxu+2Y1oa+J0nvceVwT6lry7CZLPezcZNM+5YBsML69V3jfcFOV28OlDcNoNk1FS2bXOX1ekb3Uxbpg7HPedO1EvpQYA0u7diO9JnIpRNG4c4puVPm58504lxU5Fr5tOkHHe0eSLmJUvqset/1NPYdiSxZZVhcOHo+HppzDo5ZcN4z3PvvKLJ72IZ080FHDt4eP2sDzzAyq+QykRFt/pHAXXPOVCsRJhoOfiq55sYcc2va1fvW/9c1McJbeWUlM++6nPN3o1zwgxu4dc5bVvtmHIz1/C3pYYnvp8o+ul0ynuoTSSyHOmjW5XnHqKvozHczysfdsSYPkLVn2LN3+t/F/2IvD42cCtPoObPVxqp25dDMvDKJLfET1E1+TGg5RB72cOUmtLp5DaEpTWeCumPzpdn9c8515EBO9j6VPWB4A1Taw13oo2ySghponBXT/levzj6H+kdMz5yshZPfXQdjOL3kzu3Zwu47ykQjmXtb2tz8megysBGJ51QKl5XtenDETnhzGGSw4egO7lhYDJ0x3bstXSF/bEFLG39/nnbUrv1LfTIOO8g8kXMSs/mOa5ZEbH30zRyJFgkYi+juXZDdenvA8GVBiqo3YjeEzdGMt829q1xoxPKLesG+fFFuEWt87VG0u3O5alC7Mg3Ktfb/Np2T7s+eOWY0hQJubn/12Ca59ciGVb9znWeUWldFkSvRDV+7B1iSlEMp7Dau1mWhudyx4707nMzpaF7svNA20F1Jkk8o/6knr8ZsZv8OihItiHz6BNasNtn9xmiehKhjV712DFnhWu67Y3b7eEnScSqfXzfGvr9seMMpqtUitaJcNzXhJWBq7PHn42JtVPQmenppdh+JbXFmHVF873vuzzHnXDr7pmMvQYVAkAmHBMP8tyrT/EkzwuovNh9nTzlmYUDhuuz7cscU/JCFUbKb88FrOIyYW61blt0iUh47yD6RReP62Dm+AtoOeP5HlYu51uRcEE2+zonvOiQkuNad1IutwoH3HTc965Zg0VDQCAikhFSschmXLO31imdAYOGlCD3lXtqylbXWIdZGiJeYfJxxK82HfuU7wpWi56vjJkwccY/NGHGf0MrbNUNHEi+jzwAACg5x1G2Kl2H7YtWwZAGRzikgTkQwhZOitW9BhnVXwvIM85kZ/UFimq2K3xVnyw+QP8e+m/cednd6a0r3n/nYeTnzvZdZ09aiyhIJyPyKKmwr5271p9WUu8xRLW3tXyzc1eaSkuI9bqfGc2bk2stdGtwdAB2rVpv0/L4GiaMfaSaaGIWiK3EziaiPZhds5Je/cCsnH97rz7btdthBLjmuctrYAprF1w08fponQuqykPsNc5z0s0Y9JLtV1Fq3Pe2Yxzeym11q8ML91+TZzLRUldblEF4AoLlVrTKprnXOxnjFDva/POM7t15q34+9F/R01RTfIHD6vnXD8GBtSXt09Y6NkrZ1jmH/tkvfcxJPCcf7R6l+/6fEGsqECoyl0cMp0Meudt9H3gfpTOnIHhy5ai4nij/rlWNUGDFRWBS1Juh7Vr2I3z5uSqAFioHw3sWWPMk+ecyFM0Q7dVatVrj5uN3KC8s8FfTFLbt8be6F7MXzXfs71fWLvmOTenhdnD2rsaB508EEd8V/E2Lv1wC5p2OcvOrvhsO95/coWvwKpsep9uW9Pk2S4Z9Jxzm0NpynH9Mebw3hgyuT4tn0PkL3bjnEuGob3/nXcgR6PY88QT4LKsiyebFd2lxkY9rL3vgw/mRzRfB5EHvbPORWfwnJfOnIG2pUshJjA69Bs3/7+yBbt3wFwqLr5d8USLlZWO7eRWVRCusBBMFKGXJlV/p/DgodAebRfO6O/5+WWRMkyun5zawcMabj5nlPKC/XDVLhS1M1y8T3UxjhjWTffGe+WVyzIHRcSll3D37p7rpF07bfO7ILe0IOJTIz1nsBscf079uneUYivIzWoYBJEILTe7Nd4KQfWxvLrOvZSnHxv2bfBdb/eU//KDX2J7i3fKlV+NcrfSn6v3rkZrvBX9K/rjuROfS3C0nY9QWMSwaT3wxj+XYt+uVlTUFbu2W/jGBlT3KMGImT1d1+/cYHjL05Vzrg0GMFuftbA0jFmnDUnLZxD5jdU4b4JQZh3w3nXf/dj55z9DKCpGSO2j8FgMYlUVpD17sOeRRwAA4b59UTLVWo63q9O5XJp5QGfIOa+75hoMeuN1hOsTjJyK+Zlzngg/RdqwWsMcktPz3fSs0vlghUUWUQxtm4I6wxNeXZK5khKa57ysMISKIuNz/MLQg3LndwwhLi/7+4v1Sm5k3+piHDcmPysW5BNCSYljGW9uzm219mNuU/7HbIrszTahxVgS3sK9m6zzEefvQhD5gGYEb9q/KUFLf8xlQt1C1u2ecz/DHEi+gshtn9yGV9e9ijV71yRu3AXwq2u+J0B4OwCUpkkxXfOcu4ypEISCyTiXDxwAJGsfUtqtRLqZverxbdscwnGBhOS6GHTbdTBiJ/CcM1EMWLdZNc86eVh7xUkn6dNayI5VgVKhZaES/i4UFljCd7QQ94JiI98m6uF1Tgea57woLHp6t1OlypR3/txXm3DPWysdbbR65et3N2N83+RCvp+67KB2HV9XhEXcO2s5HdZerOTUJqxtvs+l7KDLwBgAYN371nkfLx9B5DKacX7bJ7f5eqsTYR44Nwu1adiNcy/+fvTfMW/gvKRTrc4Zfk5S7Ts7sTafAXKffPzSqgJU91QGGwvSNLBvhLV3rv4bkUZMqRbNn32GXfc/YF2vXTuyrL+XWxYudFzL5lB3QoHuugwya3CtY1moKz3otJz0TibysmDLAst82ZFH6tO6cR7zzhlnoZDFc64RCQl4rc8kPDJkdsKc7PZwxyvLASiq53H1HBWFRXzv4AF+myXN2l3N+meZecSUix5OMpJkQpLGPAHP+y+n87vCqsHRmkAQUHa5z4LksN64p9M9l4iug1kVfW9b6qKZZsPLLac5kQAcAFwy+hJMrp+MW2be4hq67se2ZqVayLi6cUlt11nRjPO5l492rPPznHOZI1ygPM+lWHr6Du88ory7SfiN8EI0hbE3L1iA1m++sTbQnJFcBjf1aePbrFWCeFvX1Z3wogtZih3PhTOdecOdIaw9MNrLvhN0gu+dfa8+vXrvasu6ssMPQ7cfXwsAkNT65m6eczPMZZAmEhLwu4ln4KERxyCWQeNco7E5ii17W8E5R0tM6rASZWt2Gp2MZNV57eI0RAC8fuNcDmsPqVEkiQTgPrnPuSwe4EXflQZJiU5HUciIsvrZ+z9LaR+zHpuFx5Y95tsmiHHeqzRIFJ3Co8c+apl/bd1rAIC+5X0D76MzoxnnvYY6B6FrenpXl5BlrquopyvnXIPC2gkvhJISDFv6DcRapyMSAJh68XBJBveKaIPTWCfIOM8ogkunuDMIwgVFr4PZCb7y9F7TMahykOd6uxCGuXajmfK5c5QJN8+5qWRJe8LNt+xtwcY9zQnbNbXG8cma3WiLG97zTOAXBfDPD4xcwzOn9MnI53d5PFR+czqsPawZ52qOeYFH2cD9LjmwbqrVptxanHSvcz1B5BFu9cR7lATX77j9k9vR2NaIb/d8qy+767O7HLnfQcLauae6iJNRtaNclz+3quuJwbkRV43zcIGISKH1fVxa5Z1LLps854vf3pjWY6IBccIPxhiEiIcOkzYIzmVLyTQiMWScZxC3R1pnyDkPilipdKgLh3QOZc9uxUp98wEVzvBvh6Hj4jkXSkoQqqsD4B7GY/Yir9+d2Lg2s3L7fry8RMm/Peh/38TM29/ybDtjUA2KI8aLvyWqPDSLwul5HPSpttaqXLHdve7qZz+fbbEbrzh0EH5zonvnjUgdr9qhLJc954WqMb5vq/L/O//waFfuLOloN86nXg6MONGY9ylJRBD5gFnITSOZkPKHlz7sWDZ/9XzM++88y7Ko7O05rypQvLtmL34Q3j7tbbx92ttJbdPZmXGqMvAfa5MgCAyMMZRWW7UE4lHvQW4ucYQiyvlPVyk1DbtaO0HYiW3ebJkvHDkS4b59wVSH0457/mIps0YkhozzDOIWTermTe+sFA4dir4PPohu11+f7UNJC8UhpczJFeOucKyL77B68NwELng8rqtbSnsUxfJIf2vqw7JfHwMAeHOZvyqundm/eweXPfxFwnYN17+AD1buwsC6Uhw+rBsG1JZg+z5loCBdA0ev/c8hGNXLKFPlFaJfUxLBLScZuXWMAQUheiSlm6ozz0DNpZfq8wVDhyoTuew5L6pU/jepatSREqDfDGe7L/4FfHq/dZk9rJ3LQMjsdSLjnCCCEJO8Pec/nfpTXDf5OhzTcExS+6wpqklaOK6zU9VDEXOLtUkQQsp7+PirxlnabF/vbXTLMocopu/dadYfIM85kSyh+npLH5g3N7s6rAhvqCfcAcwcVIsZg5SXUReyzQEAJVOneIe85Bk/nPhDTO85HbN6zXKutIWpb7/zLuz+178sy3g8DhayKqmK1dWWeb+8b0nm2HMgcQ5gECIhARVFYcRljttfXgYAmL/IRfk6BQrDIsoKjO/Z6iFQwxhDq6l8W0QUUByxGoz9a0tw7rR+aTmurgoLh1FzwXf1+VCN+izKaUE4td7vATWsPVwEbPrcWD/6O8b0S9dZt7V7zuUYYC5/WEyGAdH58DOkk2Xt3rXKPn3C2isLKnHuiHMhCu1/jjw056F27yOf0UTXtqzaC1EdoC6tKsD0k41UuuJy736ULHEUqesLits/6KqnJII850TyCIWF4LEY5KjSXy2dfQR5zpOEjPMMMqlfNab0r8bPjxue7UMh0kCf8j6498h7UawZDmZcvMPbbv1ffZpzDkiSotRuou6qqxzbdS93zy275YWlGP/r17DXpxaqH+bR8H2tMRSGRbTEJD2sPVnldD/MXnjOOTjnuOetlVi706o4O7yH4WHvVl6Iooj1kfTWtYfiysO8c/2JYLAC45o68OGHyrJwDnvOtRDdte8p/8MlVqPbbJzbsXvOhx5rVX0ffFR6jpEgssiEbhMs834h6MnwwuoXcPx/j8cHmz7wFYRLNpzdi/qSeozrNi4t+8pXzF5vszr6+KMMobxFb27EjvX7sHGZUyRTljlCYQGlVQXoP9ZdnCsZzJk/IkWzEUkiFBeDR6O691wsKQGXyThPBrrrMkhRRMQTlx6EYfXlhnB5Z1BHIxz4KVECANSHlN0gEivKHU23NSnGxeod1lztv6viaXtbvI1zSfYO2Y2aBhBiEkdRWERrTMIJ43oCAK49aqjfN0gKcyicJHPsPhDFHa8sx6F3vm1pV19hzasrcjEYI9Q5aDdm41wnDR6vjGHPn43YB8RMz9FeE62r7J7zwbOBr/9j2pSewUT+c9zA4yzzQWuSJ2LJziUAgJWNK3336SZKlwxnDjsTALD1wNZ27aczYPZO+xnDT9z6KZ79w1eWZVzmAFeMejEkQIq3P21H85xPOzG95VWJrkF0wwbITU2Ib1PSM+Vo1FcQbvB773bUoeUN1OsliDQglnqXOQGM0mp2z7mfoXD4Xe8kfRxRUxkVu0q6ed2anQdQFBHQEpV0HYS6svZ1tsyYqxK0STKe/WqzZ9uXrp6Fj396BACgwCRK98SlByn76krlBzMEEwT0+t1dGDDfUEVmkbDPFlnGbpyHi4zyaoC1FFrEdu9pnvMT/gKcZk0tIYjOwqmDT7XMByl7FgRNfV1ggq9xXhgq9FwXhHQdb2egumeJPm2vK37lXw/33VZWDWkmMAghAXIaSqnpziQKaSdSoPnjjwEA+998EwDAozFwSULhiBGu7TWhZMKAjPMOohOV/CZcqDrrLJQff7zner3uuc0459HgHRRNLM2vdM0zX27Sp6M249xenq0oLCIuc1z39CIA6fVQm4UP22Kyr7d/eI9y3YNuzkGf0l/Jxw9TTeq0UD53LgoGD0bB4MEAAPlAchUBOhT7gzJcApxr8n7X+KQ6aJ7znuOAESdY1/UYm5bDI4hsw2z3SEyOYfSDo3Hfovvatd8dzTuU/YPpBvQfD/ujo117w9pb4i3t2r4zUVAU0j3mQcLI59/9Fe65TDF8NONcEBj2bDmAtUt2tft4NM+5/RojiFTg0SggxRHu1cuxrnyed7+5K0O9XoJIAywcRumhh3iuj21Uao/Gt++wLJdbXGoye6B5o1ti1vCga59cqE/vaTaM/Wjc23MOOMXnImlUezXvqi0uYVyfykDbTexX5ViWzlx4AmhbsQIAcODjj7J8JD4wW8i9GAaKTOKJgtnrbxus0jznZs/egEOV/9OuTNcREkROcveXdydsM73ndM91r657FYBimMXkGBgYDutzGE4ferqlXURsn9BrqzqINqZuTLv201korlB+z8ZtiQdN13+t5J1zziGrg+4t+5R3v+QhwJoMmj4NKbUTQej1+98h3K+v53ol/zxuSessHDUKRZMmonsnqeaUbsg47yA0byc96jovkb7equJ7Hn0MALD3OSWsuGSGUhaKR531zm88zj3054Aq3HbbS8ssy5/6fKM+fccry/Vpu+fcbpwXRawGUDiNxvn+NiMHf9f+KJZs2uvT2qAgJGJEj3L88Yxx+rJ0lXgjFCpPOw0AUDJ5cpaPxIewLWSWMYCbBqUqnCPwOprn3Fw+rUzRVYBM5VwIgoEhxIyO8vMnPY/ySLmjTUyOISJGwBjDDyb8AEf2O1JfHxHaZ5y/uUHx/M7uO7td++kshNT3MU8iZbx5bxQtTYpRHjPVQY+2xCG1I7yda5vSq5cIQPmcOeh77736fO1V37es59GootYuhlAyXRkYjDQ0oOHhhxGyVSwiFMg47yAG1Cl5kRXFOZznSbSLgqFDPNdpSpVMDdEunjwJgFHWysx3pzf4fs7by3f4rtf4erO1LmrUVMrizu+MhV07rqQgferdH6w0Qutufv4b3PXat5b1DTUuivcqL149CyeMM4wvCq1LL1VnngEAKJl1cJaPJElqPe4ve2/WzXM++WLl/wDv6BaC6AxMrk886BaX4ygwDV71K++HuiJr3idjDDEphrAapVIeKcfvDv2dvj4spqcvs6d1T1r2k++E1UolZTXBc/kXv7NRN8J7DzWizu7/n3fx/J8Xem2WEPKcE8kSaWjQp2svu8yyjkejainhEPrcfx9qLr8M3W/4WQcfYX5BxnkHceNxI/DPCyZjZM+KbB8KkSEcYm8A2lavQeuyZQh16wYAKJ4yBQBQ873vof+zz7oKZJhfiFv2Knl5Dde/kPTxXPCPT63Hor7E/3rORJw6sTceXbA+6X2miz+eMT5rn93VKRw+HMO++RqlM2dk+1D8OeoW67yXMeAwzl08570nAjftBSp6p+/4CCLLlIadQqSfbv3UpaWVrc1bEbdFkazau8rRLiYbxrkds+e9PQh28ccuiuY579bPWcHFCyYwxNokdXvr77hxmfegB+ccq77YjnjMXUFbF5kj25xIASZaozJ5LAYej4GFQmCiiG5XX41QlTOFkTCgp2IHURgWcejQbtk+DCKDMEHAoHfetixbPXcu1px4kh7yXveDq/S2hT6edo2V2/cnbBMULaxdE5Yb2yd7A0XlRcl5XQbUleD6OcMydDRdD5YPInvTv++/vlYt/SfZUkN047x9atIEkev8Z54iknh4H39FbzvrmtahzX7f2NDC2r085GI7SzH+avqvAAAXjb6oXfvpLITCmiCc0yI+++ZprtvU9S5DTE13CxcEPx9vP7wML9+3BB894xyQAaDnsQtUxpRIAzweB2w554Q/dOcRRBoJd+/uurxtmZInzgqSMxjO/dsneoiZGW3Zsq1NjnUaNSXWnEDNONdU2Y8b0zOpY0mG647xr5merMjbmz86FJcdMrA9h0R0FqrV2rsXvAhU9FGM8QM7AUmtCBBvU0qxCdQRIDo3PUp7YPH5i3Fkw5GJGyfgvBHnWeZvWXALvt39rafnvL2cPPhkLD5/McoiZRnZf74hqsa5m0Fc2c0jDYwB8TbDOB8+vUegz/rmgy0AgB3r97mu10LlgyjHE0QioqtXQ2psdFQrIryhO48gOoDdDz4IILXa0m0uwi7XPrkIjc1RXPfUIs/tJjdYhTY0gTjNOC8rzNyD8rKDB+K8g7wF8tKpDE90Ia5fD1z+oTJdUgv0ngRsXQzcMRB44UfK8ngrIBZQTCbRZQiZBqIGVw32bWse7H3v9Pfw2qmvAYBF7E1jya4lGTPOCSt6KbUkBq6luKx7zkMRETW9nGkOfvQaooQWH9jbhpf+uli/NuS46jmnSilEGmEhepYEhXrIBJFmhnz6iec6IZK8wm2rS17Y019sxLibX8Oije4q6JGQgH420bU2tcSKZhiP6V2J8X0r0b+2BKtvnZv0cfkhCAw3nzDKc306a6oTnZjvvQOc/7wxX1gBhE31lc2h618/o/yPt1nzzQmikxNmRqfXLdLKjKRWPTi64WhUFlaivqQeADC6drRr+/aWTCOC4ec590KWOOJtyns9XCCiZX80wRZW9u1qhSTJeOKWT7H6qx16njp5zolMwNv8U2kIA7rzCCLNiGVliPRz9xqzgMZ5hSkne+kW99AzM2b187+eMwERUUBM4tjfFkdc9ZgfiCoCQGZV9meumIG3rj20w1VZ01m2jejE9BwH9J/lvd5sOLSpKR7xVso3J7oUZs+5XejNjrZ+WLVVw8Mrh5w85x1Ds1oSzVFGxUZVvfGul+KyLggXLhDBbO/xRAM1yxdsxUfPrNI/O9aq7EuSyDgn2kfp4YoOBiswBsr3/Pvf2TqcvIPuPILIAKVHHOG6nIWDdXQ++unhmD5QKbN25v0f68t/fLSSyz1zUK2lfXPU8K73qymBKDBIsoxRv3wFP3jsS0TjMjbsbgaQ2XD2oJDnnEgLbl498pwTXYxtzdv06Zgc822rGedBjW4yzjuGDd/sBgAsfmeTb7vDzx+uT8ejMtZ/o5QtDUdE1Pe3irzu35PYU7nw9Q36tKCK0elh7S7idAThxcBXX0H/Z58FABSOGgkAEE11zAvHjsnKceUj1EMmiAxQd83V7dq+OBJC32qnCMy0ATWoLS2weNYB4JAhRo3a4oiIsMgQU0fgX1y8Fd/9xye481Wl1ng665knw4+ONNTpQ1Q/lUgHdiVpzslzTnQ5UvGciyyYujeFtecWZnG4NQt3YP3XilEvhJhDsX3nxuSqvWiOdj2snSLciCSI9O2rVyGqvfRS9HvkERSNG6uv73HTTVk6svyD7jyCyABCJOJQpiyZ5ROe68JHq3c5lg3uXoqd+9vwwuItluXm/O6isAiBMTxiqmP+4SpjX8Xh9pXASZWrjjCEihiJdRHpwG6cb/uaPOdEl2Nqj6n6dCLPeXNciaAqCHiP2D3nvUt7J3l0RBDmXKrk/JdVuw8sHn7eMJRWF6CgOIQr/nIYAKOWeSgsgDGncf7iXxbhlQeW4Lk/fonFb29MeAyyGs5OOedEe2GiiOIJ4y1pGiGPakaEE7rzCCJD2MXfhGKPcigerNvVbJlffetclBe6hxgWRYyXcmFExPZ93uFsHZ1fDigDBgSRduwht3+dQZ5zosvRvdjo9O5u3e3btlVqBQCUhp3K3mcPPxsAMKrGGOy1G+fPnvgsPjnbW/SUSI0eg5WQ9No+7orrw6f3xPm3zgBjDExguoAcAMRjhiicnZWfbceGpXvw7mPf6suKy92jIdYu3AnAXOecBtGJdiIbKZdBNZcIMs4JImPY88vbvv3Wo2UwvIzqgXUllvlcNIS/+mX76/AShIMNC5zLyHNOdDFCQgh/OeIv+vyW/UZk1f7ofoswWExSPOtuueTXT7ke75/xPh6a+5C+LGyLTomIERSFiuybEu2kqDSCU66biNkXjAjU3s0QDxcGe/drxredZR9vxT2XvamLzFFYO9Fe5AMH9OlUqhV1VejOI4gMIe21ljmrPv/8pLZ/8rKDXJdXl1gfcFoO+ROXHoQbjxuBsCjgyBG5ET70i+NGYEBtCQpCSqfhz2eNx5WHDczyURGdhrXvOZeR55zogszqbaRNPfntkwCADfs24KBHD9LnASPs3UvoraKgwpLDToJwHUf9gApEAgq2RlwMcc0j3mtIped2jdub0XrAP/VBWy+GyXNOtI/Wb1fo0+Q5Dw4Z5wTRQVSeflpS7c0h7N87eIA+fcWhVuNWK0s2pX81LpzZH0DiEiodxUUz++PNaw/V548b0xM/PnqY9wYE0V5aGslzTnRp7l98P0Y/OBpvrn8TAPDOxnf0dbpxbtdrsFFbpFQEIeM8NwlFnMY5YwxX/vVwnPjDCa7b3HPZm/j3jR+7rjMTV6u/COQ5J9qL2hcd9O47CRoSZujOI4gOIlkRNHO5sZ/OMQzanpXWkMKw6Nzv60u3J3l0BJGHXLMYGH8OMPtXxrI9a8hzThAA7vzsTgDAuxvfxab9mxCTY4hKSk3rREb3zhYl/1jLUSdyi8ZtzYkbpYhWgo0E4Yj20v1nP4VYV4uQqaQakRi68wgiRzGb3GbD3p56Hk5idPuv57iPqBNEXlLZFzjhHmCwTdOAjHOiC2IOR7dzzNPH4PLXLk8Y1k7kB+a88dIqZ6TQOb8+CEOn1qe07yVqrXXBZeCfIJKh4thjMeS998BC2Snhm6+QcU4QOUrf6mJcevAAvP7DQyzL7R74iItxbs9LB4ClNx+DY0b1SO9BEkQuUFxjnaewdqIL8uJJL/quX7B1Aa579zoAweuXv7L2lXYfF5FZNE+3mYq6IvQYVJHS/mp6KYrxZkV4giA6DrrzCCJDRPr3b9f2gsDw07nDMaibtbSKvZza0Poyx7bPXjnDsazIJUeNIDoFZfXAec8Z8+Q5J7ogPUp74JLRl/i22RfdB4A8550JN3G4RBx+nrf2y65N+wEAInnOCSIrkHFOEHnGtAHV+O2pY/DNzUfjkYun4odHDnG06VNdjEcvmYZfnzASAPB/Z1M4O9HJ6X+wMU2ec6KLcsmYS9BQ3tDu/Vw/5XoAQP+K9g0yE5nBXErt4DOHurbRouyYSxnWYQf1wAW/nen7GW7bEQSReSgJgCAyBCtUvHd9//lPhLrVpW+/jOG0SX0AANMH1Xq2O2hgDQ4aWINzD2pI22cTRM7CGMAEgMuA7F8qiCA6K0WhIsw/aT4+2PQBLnv9Ms92Epd893Ns/2Mxf9V83HHIHek+RCINnPzjCVj5+XZMnTfAW2xWXSyGBcTbrOebMaaXXgOAXkMrsWl5o6MNQRAdDxnnBJEhet/9JzQ+/TSKp06hlxxBdARcVv7v25rd4yCILDOlxxTf9UOr3L2tGpWFlXjsuMfSeUhEGqntXYba3s6UNjMhNWe8rKoAe7b6q7sfetYwfPzsKqz6YkfajpEgiNSgsHaCyBCR3r3R7eqryTAniI6G0auN6NpoOeW9S3tjdO1ox3p6L3V+Bk3qjhmnDsKU4wf4tgsViKjsXoxjvmdcJ31HUukrgsgW5DknCIIgOgdV/ZU65xPOy/aREETWeWjOQ+hT1gc1RTV4Ze0ruPOzO7H1AEWVdBUEgWHc7L5Yu2invmzinH6Ithoh7pf8/mBr3VaV1v2UGkQQ2YKMc4IgCKJz8L23gXUfAA3+QkcE0RUY122cPn10w9E4uuFofGf+d0jkrYvRMKYWld2LMW52H4yc1cuyLlLkbgbs293aEYdGEIQLZJwTBEEQnYOiSmDYsdk+CoLIWZ48/slsHwKRBc7+1bSk2vcflz4RW4IgkoMS8wiCIAiCIAiCAGCIyREE0fHQ3UcQBEEQBEEQBABgxIye2T4EguiykHFOEARBEARBEF2cSXMbAAA1vUqzeyAE0YWhnHOCIAiCIAiC6OJMnTcAU+f5l14jCCKzkOecIAiCIAiCIAiCILIMGecEQRAEQRAEQRAEkWXIOCcIgiAIgiAIgiCILEPGOUEQBEEQBEEQBEFkGTLOCYIgCIIgCIIgCCLLkHFOEARBEARBEARBEFkmo8Y5Y+wYxthyxthKxtj1LusLGGOPq+sXMMYaMnk8BEEQBEEQBEEQBJGLZMw4Z4yJAO4BMAfACABnMsZG2JpdBGAP53wQgN8DuD1Tx0MQBEEQBEEQBEEQuUomPedTAKzknK/mnEcBPAbgBFubEwA8qE4/BeAIxhjL4DERBEEQBEEQBEEQRM6RSeO8F4ANpvmN6jLXNpzzOIC9AGoyeEwEQRAEQRAEQRAEkXPkhSAcY+x7jLHPGGOf7dixI9uHQxAEQRAEQRAEQRBpJZPG+SYAfUzzvdVlrm0YYyEAFQB22XfEOb+Pcz6Jcz6prq4uQ4dLEARBEARBEARBENkhk8b5pwAGM8b6M8YiAM4A8JytzXMAzlenTwXwJuecZ/CYCIIgCIIgCIIgCCLnCGVqx5zzOGPs+wBeASAC+Dvn/GvG2M0APuOcPwfgbwAeYoytBLAbigFPEARBEARBEARBEF2KjBnnAMA5fxHAi7ZlN5qmWwF8J5PHQBAEQRAEQRAEQRC5Tl4IwhEEQRAEQRAEQRBEZ4aMc4IgCIIgCIIgCILIMmScEwRBEARBEARBEESWYfkmjs4Y2wFgXbaPIwC1AHZm+yCIwND5yi/ofOUXdL7yCzpf+QWdr/yCzld+Qecrv8in89WPc+6oEZ53xnm+wBj7jHM+KdvHQQSDzld+Qecrv6DzlV/Q+cov6HzlF3S+8gs6X/lFZzhfFNZOEARBEARBEARBEFmGjHOCIAiCIAiCIAiCyDJknGeO+7J9AERS0PnKL+h85Rd0vvILOl/5BZ2v/ILOV35B5yu/yPvzRTnnBEEQBEEQBEEQBJFlyHNOEARBEARBEARBEFmGjPMMwBg7hjG2nDG2kjF2fbaPpyvCGOvDGHuLMfYNY+xrxtjV6vKbGGObGGNfqX9zTdv8VD1nyxljR5uW0/nsABhjaxlji9Xz8pm6rJox9hpjbIX6v0pdzhhjf1LPySLG2ATTfs5X269gjJ2fre/TmWGMDTXdQ18xxpoYY9fQ/ZU7MMb+zhjbzhhbYlqWtvuJMTZRvV9Xqtuyjv2GnQuP83UHY2yZek6eYYxVqssbGGMtpvvsr6ZtXM+L17knUsPjfKXt+ccY688YW6Auf5wxFum4b9f58Dhfj5vO1VrG2Ffqcrq/sgzz7sN3jXcY55z+0vgHQASwCsAAABEACwGMyPZxdbU/AD0ATFCnywB8C2AEgJsAXOvSfoR6rgoA9FfPoUjns0PP2VoAtbZlvwVwvTp9PYDb1em5AF4CwABMA7BAXV4NYLX6v0qdrsr2d+vMf+o9shVAP7q/cucPwMEAJgBYYlqWtvsJwCdqW6ZuOyfb3zmf/zzO11EAQur07abz1WBuZ9uP63nxOvf0l9bzlbbnH4AnAJyhTv8VwOXZ/s75/Od2vmzr7wJwozpN91f2z5dXH75LvMPIc55+pgBYyTlfzTmPAngMwAlZPqYuB+d8C+f8C3V6H4ClAHr5bHICgMc4522c8zUAVkI5l3Q+s8sJAB5Upx8EcKJp+b+4wscAKhljPQAcDeA1zvluzvkeAK8BOKaDj7mrcQSAVZzzdT5t6P7qYDjn7wLYbVuclvtJXVfOOf+YK72cf5n2RaSA2/ninL/KOY+rsx8D6O23jwTnxevcEyngcX95kdTzT/XgHQ7gKXV7Ol/txO98qb/3aQAe9dsH3V8dh08fvku8w8g4Tz+9AGwwzW+Ev1FIZBjGWAOA8QAWqIu+r4a9/N0UeuR13uh8dhwcwKuMsc8ZY99Tl3XnnG9Rp7cC6K5O0/nKHc6AtVND91fukq77qZc6bV9OZI4LoXh3NPozxr5kjL3DGJulLvM7L17nnkgv6Xj+1QBoNA3M0P2VWWYB2MY5X2FaRvdXjmDrw3eJdxgZ50SnhjFWCuBpANdwzpsA/B+AgQDGAdgCJZSJyA1mcs4nAJgD4ErG2MHmleroJpWXyCHUPMh5AJ5UF9H9lSfQ/ZQ/MMZuABAH8G910RYAfTnn4wH8EMAjjLHyoPujc58x6PmXn5wJ6wAz3V85gksfXqcz/85knKefTQD6mOZ7q8uIDoYxFoZyU/+bc/4fAOCcb+OcS5xzGcD9UMLKAO/zRuezg+Ccb1L/bwfwDJRzs00NP9JCyrarzel85QZzAHzBOd8G0P2VB6TrftoEa4g1nbcMwRj7LoDjAJytdkahhkfvUqc/h5K3PAT+58Xr3BNpIo3Pv11QwnJDtuVEmlF/45MBPK4to/srN3Drw6OLvMPIOE8/nwIYrCptRqCEfD6X5WPqcqg5RH8DsJRz/jvT8h6mZicB0JQ7nwNwBmOsgDHWH8BgKGIRdD47AMZYCWOsTJuGIoS0BMpvralrng/gWXX6OQDnqQqd0wDsVUOdXgFwFGOsSg0pPEpdRmQGi8eB7q+cJy33k7quiTE2TX3WnmfaF5EmGGPHALgOwDzOebNpeR1jTFSnB0C5n1YnOC9e555IE+l6/qmDMG8BOFXdns5X5pgNYBnnXA9xpvsr+3j14dFV3mHJqMfRX2CVwblQlAVXAbgh28fTFf8AzIQS7rIIwFfq31wADwFYrC5/DkAP0zY3qOdsOUyqjXQ+O+R8DYCiVLsQwNfa7wwl9+4NACsAvA6gWl3OANyjnpPFACaZ9nUhFMGdlQAuyPZ366x/AEqgeHgqTMvo/sqRPyiDJlsAxKDk012UzvsJwCQoxscqAH8GwLL9nfP5z+N8rYSSL6m9w/6qtj1FfU5+BeALAMcnOi9e557+0nq+0vb8U9+Jn6jXwJMACrL9nfP5z+18qcv/CeAyW1u6v7J/vrz68F3iHaZdVARBEARBEARBEARBZAkKaycIgiAIgiAIgiCILEPGOUEQBEEQBEEQBEFkGTLOCYIgCIIgCIIgCCLLkHFOEARBEARBEARBEFmGjHOCIAiCIAiCIAiCyDJknBMEQRBEF4Mxdg1jrDjbx0EQBEEQhAGVUiMIgiCILgZjbC2UWrA7s30sBEEQBEEokOecIAiCIDoxjLESxtgLjLGFjLEljLFfAugJ4C3G2Ftqm6MYYx8xxr5gjD3JGCtVl69ljP2WMbaYMfYJY2yQuvw76r4WMsbezd63IwiCIIjOAxnnBEEQBNG5OQbAZs75WM75KAB/ALAZwGGc88MYY7UAfg5gNud8AoDPAPzQtP1ezvloAH9WtwWAGwEczTkfC2Bex3wNgiAIgujckHFOEARBEJ2bxQCOZIzdzhibxTnfa1s/DcAIAB8wxr4CcD6Afqb1j5r+H6ROfwDgn4yxSwCIGTtygiAIguhChLJ9AARBEARBZA7O+beMsQkA5gL4DWPsDVsTBuA1zvmZXruwT3POL2OMTQVwLIDPGWMTOee70n3sBEEQBNGVIM85QRAEQXRiGGM9ATRzzh8GcAeACQD2AShTm3wMYIYpn7yEMTbEtIvTTf8/UtsM5Jwv4JzfCGAHgD6Z/yYEQRAE0bkhzzlBEARBdG5GA7iDMSYDiAG4HEp4+suMsc1q3vl3ATzKGCtQt/k5gG/V6SrG2CIAbQA07/odjLHBULzubwBY2DFfhSAIgiA6L1RKjSAIgiAIV6jkGkEQBEF0HBTWThAEQRAEQRAEQRBZhjznBEEQBEEQBEEQBJFlyHNOEARBEARBEARBEFmGjHOCIAiCIAiCIAiCyDJknBMEQRAEQRAEQRBEliHjnCAIgiAIgiAIgiCyDBnnBEEQBEEQBEEQBJFlyDgnCIIgCIIgCIIgiCzz/3eJhAgdkNe9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drifting_env = DriftingBandit(n_actions=5)\n",
    "\n",
    "drifting_probs = []\n",
    "for i in range(20000):\n",
    "    drifting_env.step()\n",
    "    drifting_probs.append(drifting_env._probs)\n",
    "\n",
    "plt.figure(figsize=(17, 8))\n",
    "plt.plot(pd.DataFrame(drifting_probs).rolling(window=20).mean())\n",
    "\n",
    "plt.xlabel(\"steps\")\n",
    "plt.ylabel(\"Success probability\")\n",
    "plt.title(\"Reward probabilities over time\")\n",
    "plt.legend([\"Action {}\".format(i) for i in range(drifting_env.action_count)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your task is to invent an agent that will have better regret than stationary agents from above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR AGENT HERE SECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-294-2cb5770fc47a>:5: RuntimeWarning: invalid value encountered in sqrt\n",
      "  np.sqrt(2 * np.log(self._total_pulls + 0.1) / (self._successes + self._failures + 0.1)))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drifting_agents = [\n",
    "    ThompsonSamplingAgent(),\n",
    "    EpsilonGreedyAgent(),\n",
    "    UCBAgent(),\n",
    "#     YourAgent()\n",
    "]\n",
    "\n",
    "regret = get_regret(DriftingBandit(), drifting_agents, n_steps=20000, n_trials=10)\n",
    "plot_regret(drifting_agents, regret)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2. Exploration in MDP\n",
    "\n",
    "The following problem, called \"deep see\", illustrates importance of exploration in context of mdp's."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"deep_see.png\">\n",
    "\n",
    "The deep sea problem is implemented as an $N×N$ grid with a one-hot encoding for state.\n",
    "The agent begins each episode in the top left corner of the grid and descends one row\n",
    "per timestep. Each episode terminates after N steps, when the agent reaches the bottom\n",
    "row. In each state there is a random but fixed mapping between actions $A = {0,1}$ and\n",
    "the transitions ‘left’ and ‘right’. At each timestep there is a small cost $r = −0.01/N$ of\n",
    "moving right, and $r = 0$ for moving left. However, should the agent transition right at every\n",
    "timestep of the episode it will be rewarded with an additional reward of $+1$.\n",
    "\n",
    "**Question:** Why is the deep see a challengin exploration problem?\n",
    "\n",
    "See full paper [here](https://openreview.net/forum?id=rygf-kSYwH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[37mLoaded bsuite_id: deep_sea/0.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'size': 10, 'mapping_seed': 42}"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gymnasium as gym\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from torch import nn\n",
    "from time import sleep\n",
    "from tqdm import tqdm\n",
    "from IPython.display import clear_output\n",
    "\n",
    "from q_learning_agent import QLearningAgent\n",
    "from replay_buffer import ReplayBuffer\n",
    "\n",
    "env = gym.make(\"bsuite/deep_sea-v0\", size=10, seed=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Epsilon-greedy q-learning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_state_number(s):\n",
    "    return np.argmax(s.flatten())\n",
    "\n",
    "def test_agent(agent, greedy=True, delay=.5):\n",
    "    v = get_all_states_value(agent)\n",
    "    s, _ = env.reset()\n",
    "    done = False\n",
    "    while not done:\n",
    "        fig, ax = plt.subplots(ncols=2)\n",
    "        ax[0].imshow(s)\n",
    "        ax[0].set_title('State')\n",
    "        im = ax[1].imshow(v)\n",
    "        plt.colorbar(im)\n",
    "        ax[1].set_title('Value function')\n",
    "        clear_output(True)\n",
    "        plt.show()\n",
    "        s = get_state_number(s)\n",
    "        if greedy:\n",
    "            a = agent.get_best_action(s)\n",
    "        else:\n",
    "            a = agent.get_action(s)\n",
    "\n",
    "        s, r, terminated, truncated, _  = env.step(a)\n",
    "        done = terminated or truncated\n",
    "        sleep(delay)\n",
    "\n",
    "def get_all_states_value(agent):\n",
    "    s_shape = env.observation_space.shape\n",
    "    s_shape_flatten = np.prod(s_shape)\n",
    "    v = np.zeros(s_shape_flatten)\n",
    "    for i in range(s_shape_flatten):\n",
    "        v[i] = agent.get_value(i)\n",
    "    v = v.reshape(s_shape)\n",
    "    return v\n",
    "\n",
    "def to_one_hot(x, ndims):\n",
    "    \"\"\" helper: take an integer vector and convert it to 1-hot matrix. \"\"\"\n",
    "    x = x.long().view(-1, 1)\n",
    "    x = torch.zeros(\n",
    "        x.size()[0], ndims).scatter_(1, x, 1)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent = QLearningAgent(\n",
    "    epsilon=1, \n",
    "    alpha=0.5, \n",
    "    discount=1, \n",
    "    get_legal_actions=lambda s: range(env.action_space.n)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_agent(agent, greedy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to solve this by q-learning with high epsilon!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(5000):\n",
    "    s, _ = env.reset()\n",
    "    done = False\n",
    "    while not done:\n",
    "        i_s = get_state_number(s)\n",
    "        a = agent.get_action(i_s)\n",
    "        s_next, r, terminated, truncated, _  = env.step(a)\n",
    "        done = terminated or truncated\n",
    "        i_s_next = get_state_number(s_next)\n",
    "        agent.update(i_s, a, r, i_s_next, terminated)\n",
    "        s = s_next"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_agent(agent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But if we do bigger env:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[37mLoaded bsuite_id: deep_sea/1.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'size': 12, 'mapping_seed': 42}"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env = gym.make(\"bsuite/deep_sea-v0\", size=12, seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent = QLearningAgent(\n",
    "    epsilon=1,\n",
    "    alpha=0.5,\n",
    "    discount=1,\n",
    "    get_legal_actions=lambda s: range(env.action_space.n)\n",
    ")\n",
    "\n",
    "for i in range(5000):\n",
    "    s, _ = env.reset()\n",
    "    done = False\n",
    "    while not done:\n",
    "        i_s = get_state_number(s)\n",
    "        a = agent.get_action(i_s)\n",
    "        s_next, r, terminated, truncated, _  = env.step(a)\n",
    "        done = terminated or truncated\n",
    "        i_s_next = get_state_number(s_next)\n",
    "        agent.update(i_s, a, r, i_s_next, terminated)\n",
    "        s = s_next"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ya/QyCusbMGThNoZC5jhFtzxAwaGJzESP2FnmOEUXBqt10UQCSMQomeMU3UoLJCK2G2+OU0SdDVu3JYBEBOPPcapPkJ2z//7TULuYiTIKKyImnONke5nthbYXztl7r/5XMGYcU73CarINirRAIkbJHKfozvB1oqcFErGzzHGKrtjNtkGRFkjEKJnjFN0apNdTTaQFEhHRgmoU1i6NtolIOlHSHZLWSjq3w/HdJV1djt9QRgsi6WWS1kj6dvn3pbVzri/XHGlVP7HXZ04LJCKiJW28npI0B/gQ8DJgPXCjpBWjJrKeAWy2fbikxcBFwGuBjcDv2P6RpGcBK6nmMI14XVkbvRVpgUREtKSlUVjHAGtt32n7UeAqYNGoMouAy8rn5cBxkmT7Zts/KvtvAx4nafeWHm8nCSARES0wzYJHCSDzJa2ubUtrlzoYWFf7vp4dWxE7lLG9BbgPOGBUmd8DbrL9SG3fx8vrq78sow17kldYEREtmcQbrI22F05VPSQ9k+q11gm13a+zvaHkd/s01UjDy3u5T1ogERFtMHibGm0T2AAcUvu+oOzrWEbSXGBf4N7yfQHwWeCNtn+wvXr2hvLvA8AnqV6V9SQBJCKiJS31gdwIHCHpMEm7AYuBFaPKrACWlM+nANfZtqT9gH8EzrX9zyOFJc2VNL983hU4GfhOr8+bV1gRES1pYxSW7S2SzqYaQTUHuMT2bZIuBFbbXkGVKeEKSWuBTVRBBuBs4HDgfEnnl30nAA8BK0vwmANcC3y017r2HEDKkLPVwAbbJ/d6vYiI2WgkF1Yr17KvAa4Zte/82ueHgdd0OO+dwDvHuOzzWqlcTRstkD8Gbgf2aeFaERGzk4HMRG+udNa8Eri4nepERMxeyYU1Oe8D/hR4/FgF6usm7MGePd4uImKmajTCaqB03QKRdDLwU9trxitXXzdhV6ZsQmRE6yTNkXSzpC9Md11ilnDDbUD00gJ5IfCqkuZ6D2AfSX9v+/XtVC1i2qV/L5pzsvE2Zvs82wtsH0o1hOy6BI8YFOnfi64MWQskEwkjOnsfVf/etk4HJS0dyWO09cGH+lqxmMnUcBsMrQQQ29dnDkgMiib9e1kTPTra1nAbEJmJHrGz9O/F5GUeSESkfy+6lXkgERHRnQEKDk0kgESMw/b1wPXTXI2YLYbsFVYCSERES5QWSERETJoFQ5bKJAEkIqItaYFERERXEkAiIqIrCSARETFpmUgYERHdkpttE15HOlHSHZLWSjq3w/HdJV1djt8g6dDasfPK/jskvbzpNbuRABIR0ZYWsvFKmgN8CDgJOAo4TdJRo4qdAWy2fTjwXuCicu5RVNkTngmcCPxtWdemyTUnLQEkIqIlLbVAjgHW2r7T9qPAVcCiUWUWAZeVz8uB4ySp7L/K9iO2fwisLddrcs1JSwCJiGiL1WyD+SPLAZRtae0qBwPrat/Xl310KmN7C3AfcMA45za55qSlEz0iog2TWyxqo+2FU1eZ/kgLJKIDSftJWi7pe5Jul/SC6a5TzALtrEi4ATik9n1B2dexjKS5wL7AveOc2+Sak9ZTAMkPWQyw9wNfsv0M4DlUa6NHjEvbmm0TuBE4QtJhknaj6hRfMarMCmBJ+XwK1ZIDLvsXl1FahwFHAP/W8JqT1usrrJEfslNKpfbstUIR003SvsCLgdMBSqfjo9NZp5glWphIaHuLpLOBlcAc4BLbt0m6EFhtewXwMeAKSWuBTVQBgVLuU8B3gS3Af7W9FaDTNXuta9cBJD9kMcAOA34GfFzSc4A1wB/b3r74een0XAowZ//9p6WSMbM0nePRhO1rgGtG7Tu/9vlh4DVjnPsu4F1NrtmrXl5h1X/IbpZ0saSdFoeWtHRkpMFjPNLD7SL6Zi7w68CHbT8XeAjYYeJV1kSPjpqPwhoIvQSQCX/IYMcftF3ZvYfbRfTNemC97RvK9+VU/78eMb52OtFnjV4CSH7IYiDZ/jGwTtLTy67jqN4pR4yrrVQms0XXfSC2fyxpnaSn276D/JDFYPlD4BNlcMidwJumuT4x07nRCKuB0usorPyQxUCyfQsw6yd6RZ8NUOuiiZ4CSH7IIiJqEkAiIqIbg9S/0URSmURERFfSAomIaMuQtUASQCIi2pBRWBER0bW0QCIiYrLE8HWiJ4BERLQlASQiIiZtwNKUNJEAEhHRlnSiR0REN4atBZKJhBEdSPoTSbdJ+o6kKyXtMd11ilkg6dwjhpukg4E/AhbafhbVEqCLp7dWMeM1DR4JIBEDby7wOElzgT2BH01zfWIW6Md6IJLmSVol6fvl345rKktaUsp8X9KSsm9PSf8o6Xulhf3uWvnTJf1M0i1lO3OiuiSARIxiewPw18C/A/cA99n+cr1MfanmrQ8+1OkyMYz60wI5F/iK7SOAr9BhJVhJ84ALgGOBY4ALaoHmr20/A3gu8EJJJ9VOvdr20WW7eKKKJIBEjFJ+0BYBhwFPBvaS9Pp6mayJHp1oW7OtR4uAy8rny4BXdyjzcmCV7U22NwOrgBNt/8L2VwFsPwrcBCzotiIJIBE7Ox74oe2f2X4M+Azwm9Ncp5jpJtcHMn+kBVu2pZO404G27ymffwwc2KHMwcC62vf1Zd92kvYDfoeqFTPi9yTdKmm5pEMmqkiG8Ubs7N+B50vaE/gl1XLNq6e3SjHTqWwNbbQ95mJ8kq4FntTh0NvrX2xbmnyvSunbuxL4gO07y+5/AK60/YikN1O1bl463nV6CiCS/gQ4kyqmfht4k+2He7lmxHSzfYOk5VTN+y3AzcCy6a1VzAotjbCyffxYxyT9RNJBtu+RdBDw0w7FNgAvqX1fAFxf+74M+L7t99XueW/t+MXAeyaqZ9evsDLUMQaZ7QtsP8P2s2y/wfYj012nmPn6MQoLWAEsKZ+XAJ/vUGYlcIKk/Uuf3gllH5LeCewLnLND3atgNOJVwO0TVaTXPpAMdYyIGNGfUVjvBl4m6ftU/XXvBpC0UNLFALY3Ae8AbizbhbY3SVpA9RrsKOCmUcN1/6gM7f0WVePg9Ikq0vUrLNsbJI0Mdfwl8OXRQx3LQy0FlgLswZ7d3i4iYmbr04JS5VXTcR32r6bqUhj5fglwyagy6xmjq8b2ecB5k6lLL6+wJhzqWCq1fbjjruze7e0iIma+zERvLEMdIyJq+tQHMmP0EkC2D3WUJKom1YSdLhERA2vIWiC99IFkqGNERM0gtS6a6GkeiO0LqPKtREQMN5MFpSIiYvJEWiAREdGtBJCIiOiGPFwRJAEkIqINAzbCqokEkIiIlgxbH0jWA4mhJekSST+V9J3avkbLhUZ00qcFpWaMBJAYZpcCJ47aN+FyoRFjGrKJhAkgMbRsfw3YNGp3k+VCI3bWMI3JIL3mSh9IxI6aLBe6Q5bpOfvnLVcUAxQcmkgLJGIMtsd84VDPMj1n7736XLOYiUYmEqYFEjG8miwXGtGRtg1QdGggLZCIHTVZLjRiZ0070AcoxqQFEkNL0pXAS4D5ktZTJQZ9N/ApSWcAdwOnTl8NY7YZpCG6TSSAxNCyfdoYh3ZaLjSikT60LiTNA64GDgXuAk61vblDuSXAX5Sv77R9Wdl/PXAQ1VLkACfY/qmk3YHLgecB9wKvtX3XeHXJK6yIiJb0qRN9wrlKJchcABwLHANcMGpS7OtsH122kX6+M4DNtg8H3gtcNFFFEkAiItpgwG629abJXKWXA6tsbyqtk1XsPGl2vOsuB44rq82OacIAknQPERHNTCKVyXxJq2vb0kncpslcpYOBdbXv68u+ER+XdIukv6wFie3n2N4C3AccMF5FmrRALiXpHiIixjXJeSAbR+YRlW2H5cAlXSvpOx22RfVy481VGsfrbP9H4EVle0O3zzxhJ7rtr0k6dNTuRVSjV6Bq8lwP/Fm3lYiImPXaeT1VLuXjxzomqclcpQ386nc0wAKq39PY3lD+fUDSJ6n6SC4v5xwCrJc0F9iXqjN9TN32gTRK9wBVyoeRZtpjPNLl7SIiZr4+daI3mau0EjhB0v6li+EEYKWkuZLmA0jaFTgZGOmeqF/3FOC60sIZU8/DeG1bGvs/SWmaLQPYR/MGaApNRMQo/fkN13GukqSFwFm2z7S9SdI7gBvLOReWfXtRBZJdgTnAtcBHS5mPAVdIWkuVZHTxRBXpNoAk3UNExCj9yHNl+146zFWyvRo4s/b9EuCSUWUeoprn0em6DwOvmUxdun2FlXQPERF1Bra62TYgJmyBJN1DxPgeXbd+413nvO3uDofmAxv7XZ8pMCjPAb09y1MnKjBImXabaDIKK+keIsZh+wmd9ktabXthv+vTtkF5DujDs7Q0Cmu26GsurCOf/QtWrrxlUue8/MlHT0ldIiLalhZIRERM3oClam8iASRi6iybuMisMCjPAVP4LAI0QB3kTSSAREyR0ekpZqtBeQ6Y+mdR+kAiImLS8gorIiK6014urNki64FEtEzSiZLukLRW0qzOVC3pLknfLqm/V093fSZjOpai6FMurBkjASSiRZLmAB8CTgKOAk6TdNT01qpnv11Wrpttc0Eupd9LUfRnQakZIwEkol3HAGtt32n7UeAqquUPos9sf40qKWBdk9X8urxhNQqryTYoEkAi2jXRSnCzjYEvS1ozyVXzZqrGS1F0xQ23AZFO9IgYz2/Z3iDpicAqSd8rf9nPehMtRdGNYRvGmxZIRLtGVnUbsaDsm5Vqq9f9FPgs1Su62ewnZQkKpmQpivSBREQPbgSOkHSYpN2oFuVZMc116oqkvSQ9fuQz1ap23xn/rBlv6paiMLCt4TYg+voKa82tj2ycc9DaSaa9XjuVVWpw/76YzvsP0rNPmG57qtneIulsqiVF5wCX2L5tmqvVrQOBz0qC6nfFJ21/aXqr1Fy/l6IQHrpXWH0NIDM17fUw33+Yn32q2L4GuGa669Er23cCz5nuenRrWpai2DZAzYsG8gorIqINfXqF1XQypKQlpcz3JS0p+x5fJoWObBslva8cO13Sz2rHzux03bqMwoqIaEmfXmGNTIZ8d8l0cC7wZzvUQ5pH9cpuIVVoWyNphe3NwNG1cmuAz9ROvdr22U0rMlNaINOd7XOY7z/Mzx7Rrv6MwmoyGfLlwCrbm0rQWMWoWfmSjgSeCHy924rMiAAy3emih/n+w/zsEe1qGDx6DyBNJkM2mdC6mKrFUa/Q70m6VdJySYcwgbzCiohog4HmaUrmj0pOuaz+x5Ska4EndTjv7TvcsrfJkIuBN9S+/wNwpe1HJL2ZqnXz0vEukAASEdGSSfSBbBxv9KHt48e8h/QTSQfZvmecyZAbqIYwj1gAXF+7xnOAubbX1O55b638xcB7JnqIvr7CmijNtaTdJV1djt8g6dAW732IpK9K+q6k2yT9cYcyL5F0X20Uwvkt3n/ctNiqfKA8+62Sfr3Fez991MiL+yWdM6pMq8/eSyrtTqNHImaF/rzCajIZciVwgqT9y8/ZCWXfiNOAK+snjMzQL14F3D5RRfrWAqmluX4Z1fu4G8uogO/Wip0BbLZ9uKTFwEXAa1uqwhbgrbZvKrNr10haNer+AF+3fXJL9xztt22PNWnuJOCIsh0LfLj82zPbd1BGXpT/HTZQpaUYrc1nvxT4IHB5bV+vo0ciZi4D2/oyCqvjZEhJC4GzbJ9pe5Okd1BlRgC40HY9M/GpwCtGXfePJL2K6nflJuD0iSrSz1dY29NcA0gaSXNd/wW+CPir8nk58EFJGtXJ05XS6XRP+fyApNupOpVGB5Dpsgi4vDzrNyXtN9JMbfk+xwE/sN0pI0BrbH+tQwtyEb9qVl9G1aT+s1Flto8eAZA0MnrkSiJmtP7kuSqvmnaaDGl7NXBm7fslwCVjXONpHfadB5w3mbr08xVWk1EB28vY3gLcBxzQdkXKL7bnAjd0OPwCSd+S9EVJz2zxthOlxe5XGvDFjP3LeKqefURbo0ciZqYhS6Y4dJ3okvYGPg2cY/v+UYdvAp5q+0FJrwA+R/VKqQ3TnhZbVXK/V9H5r4ypfPadTEUq7YhpZWBrUplMlSZprreXkTQX2Be4l5ZI2pUqeHzC9mdGH7d9v+0Hy+drgF0lzW/j3g3SYvcjDfhJwE22f9KhflP27DVNUmkPVDr0GCYGb2u2DYh+BpAmaa7rowtOAa5ro/8DqlFOwMeA223/zRhlnlTKIekYqv8+PQcwNUuLvQJ4YxmN9Xzgvino/9hp5EWtjlPy7KO0MXokYubKK6ypMVaaa0kXAqttr6D6BX+FpLVUowAWt1iFF1JNmvm2pFvKvj8HnlLq9xGqoPUWSVuAXwKLWwpgHdNiSzqrdu9rqEZFrAV+AbyphftuVwLXy4A31/bV79/qs2sSqbQnOXokYmbq3yisGUMt/YEfETHU9t3tQP/mgc3+5v3S+g+sGYRlDIauEz0iYsoM2R/kCSAREW2wYevW6a5FXyWARES0JS2QiIjoSgJIRERMnoduFFYCSEREGwweoEmCTSSARES0ZchSmSSARES0wYZtCSAREdGNdKJHREQ3nBZIRERM3mAlSmwiASQiog1DmEwxASQiogUGPGSpTPq5HkhExOByfxaUkjRP0ipJ3y//7j9GuS9J+rmkL4zaf5ikGyStlXR1WZ8JSbuX72vL8UMnqksCSERES7zNjbYenQt8xfYRwFfK907+F9UaSKNdBLzX9uHAZuCMsv8MYHPZ/95SblwJIBERbenPkraLgMvK58uAV3esiv0V4IH6vrLq6EuB5R3Or193OXDcyCqlY0kfSERECx5g88prvXx+w+J7SFpd+77M9rKG5x5YW+76x1QrnjZ1APBz21vK9/XAweXzwcA62L6C7H2l/MaxLpYAEhHRAtsntnUtSdcCT+pw6O2j7mlJ0zb0KwEkImKGsX38WMck/UTSQbbvkXQQ8NNJXPpeYD9Jc0srZAGwoRzbABwCrJc0F9i3lB9T+kAiImaXFcCS8nkJ8PmmJ9o28FXglA7n1697CnBdKT8mTXA8IiJmEEkHAJ8CngLcDZxqe5OkhcBZts8s5b4OPAPYm6olcYbtlZKeBlwFzANuBl5v+xFJewBXAM8FNgGLbd85bl0SQCIioht5hRUREV1JAImIiK4kgERERFcSQCIioisJIBER0ZUEkIiI6EoCSEREdOX/AyaTgTm9d3N8AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_agent(agent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Reward shaping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BaseIntrinsicRewardModule(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "    def get_intrinsic_reward(self, state, action, next_state):\n",
    "        return 0.0\n",
    "\n",
    "    def get_loss(self, state_batch, action_batch, next_state_batch):\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_with_reward(env, agent, reward_module, n_episodes=100, update_reward_period=100, batch_size=100, n_iter=10):\n",
    "    buffer = ReplayBuffer(size=int(1e6))\n",
    "    \n",
    "    if list(reward_module.parameters()):\n",
    "        optimizer = torch.optim.Adam(reward_module.parameters())\n",
    "    else:\n",
    "        optimizer = None\n",
    "\n",
    "    losses = []\n",
    "    s, _ = env.reset()\n",
    "\n",
    "    for i in range(n_episodes):\n",
    "        done = False\n",
    "        \n",
    "        while not done:\n",
    "            i_s = get_state_number(s)\n",
    "            a = agent.get_action(i_s)\n",
    "            s_next, r, terminated, truncated, _  = env.step(a)\n",
    "            done = terminated or truncated\n",
    "            i_s_next = get_state_number(s_next)\n",
    "            \n",
    "            state_t = torch.tensor(s).float().view(1, -1)\n",
    "            action_t = torch.tensor(a).float().view(1, -1)\n",
    "            next_state_t = torch.tensor(s_next).float().view(1, -1)\n",
    "\n",
    "            r_intr = reward_module.get_intrinsic_reward(state_t, action_t, next_state_t)\n",
    "            r += r_intr\n",
    "\n",
    "            agent.update(i_s, a, r, i_s_next, terminated)\n",
    "            buffer.add(s, a, r, s_next, terminated)\n",
    "\n",
    "            s = s_next\n",
    "\n",
    "        if (i + 1) % update_reward_period == 0 and optimizer is not None:\n",
    "        \n",
    "            for _ in range(n_iter):\n",
    "                optimizer.zero_grad()\n",
    "                state_batch, action_batch, _, next_state_batch, _ = buffer.sample(batch_size)\n",
    "                \n",
    "                state_tensor = torch.tensor(state_batch).float().flatten(1, 2)\n",
    "                action_tensor = torch.tensor(action_batch).float().view(-1, 1)\n",
    "                next_state_tensor = torch.tensor(next_state_batch).float().flatten(1, 2)\n",
    "                \n",
    "                loss = reward_module.get_loss(state_tensor, action_tensor, next_state_tensor)\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "                losses.append(loss.item())\n",
    "            \n",
    "            fig, ax = plt.subplots(ncols=2)\n",
    "            ax[0].set_title('Value function after iter: %d' % i)\n",
    "            im = ax[0].imshow(get_all_states_value(agent))\n",
    "            ax[1].plot(losses)\n",
    "            ax[1].set_title('Random network distillation loss')\n",
    "            clear_output(True)\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GoRightReward(BaseIntrinsicRewardModule):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "    def get_intrinsic_reward(self, state, action, next_state):\n",
    "        # <YOUR CODE>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent = QLearningAgent(\n",
    "    epsilon=.1, \n",
    "    alpha=0.5, \n",
    "    discount=.9, \n",
    "    get_legal_actions=lambda s: range(env.action_space.n)\n",
    ")\n",
    "\n",
    "go_right = GoRightReward()\n",
    "\n",
    "train_with_reward(env, agent, go_right, n_episodes=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_agent(agent, greedy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Curiosity-driven Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLP(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super().__init__()\n",
    "        self.layers = nn.Sequential(\n",
    "            nn.Linear(input_size, hidden_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_size, output_size)\n",
    "        )\n",
    "        \n",
    "        def init_weights(tensor):\n",
    "            if isinstance(tensor, nn.Linear):\n",
    "                nn.init.xavier_uniform_(tensor.weight)\n",
    "        \n",
    "        self.layers.apply(init_weights)\n",
    "            \n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.layers(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.1 Uncertainty with forward dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ForwardDynamics(BaseIntrinsicRewardModule):\n",
    "    def __init__(self, states_size, actions_size, hidden_size, alpha=.1):\n",
    "        super().__init__()\n",
    "        self.module = MLP(\n",
    "            actions_size + states_size,\n",
    "            hidden_size,\n",
    "            states_size\n",
    "        )\n",
    "        self.alpha = alpha\n",
    "        self.mean_reward = 0\n",
    "    \n",
    "    def forward(self, s, a):\n",
    "        sa = torch.cat([s, a], dim=-1)\n",
    "        return s + self.module(sa)\n",
    "\n",
    "    def get_intrinsic_reward(self, state, action, next_state):\n",
    "        with torch.no_grad():\n",
    "            r = # <YOUR CODE>\n",
    "            r_centered = r - self.mean_reward\n",
    "            self.mean_reward = self.alpha * (r) + (1 - self.alpha) * self.mean_reward\n",
    "            return r_centered\n",
    "\n",
    "    def get_loss(self, state_batch, action_batch, next_state_batch):\n",
    "        # <YOUR CODE>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "agent = QLearningAgent(\n",
    "    epsilon=.1, \n",
    "    alpha=0.5, \n",
    "    discount=.9, \n",
    "    get_legal_actions=lambda s: range(env.action_space.n)\n",
    ")\n",
    "\n",
    "forward_dynamics = ForwardDynamics(\n",
    "    np.prod(env.observation_space.shape), \n",
    "    1, \n",
    "    16\n",
    ")\n",
    "\n",
    "train_with_reward(env, agent, forward_dynamics, n_episodes=2000, update_reward_period=100, batch_size=100, n_iter=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_agent(agent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3.2 Uncertainty with reverse dynamics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[The paper](https://arxiv.org/pdf/1705.05363.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "class InverseDynamics(BaseIntrinsicRewardModule):\n",
    "    def __init__(self, states_size, n_actions, hidden_size, alpha=0.1):\n",
    "        super().__init__()\n",
    "        self.module = MLP(\n",
    "            2 * states_size,\n",
    "            hidden_size,\n",
    "            n_actions\n",
    "        )\n",
    "        self.alpha = alpha\n",
    "        self.mean_reward = 0\n",
    "        self.n_actions = n_actions\n",
    "    \n",
    "    def forward(self, s, s_next):\n",
    "        # <YOUR CODE>\n",
    "    \n",
    "    \n",
    "    def get_intrinsic_reward(self, state, action, next_state):\n",
    "        with torch.no_grad():\n",
    "            r = # <YOUR CODE>\n",
    "            \n",
    "            r_centered = r - self.mean_reward\n",
    "            self.mean_reward = self.alpha * (r) + (1 - self.alpha) * self.mean_reward\n",
    "            return r_centered\n",
    "\n",
    "    def get_loss(self, state_batch, action_batch, next_state_batch):        \n",
    "        a_pred_proba = self.forward(state_batch, next_state_batch)\n",
    "        a_one_hot = to_one_hot(action_batch, self.n_actions)\n",
    "        return -(torch.log(a_pred_proba) * a_one_hot).sum(dim=-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "agent = QLearningAgent(\n",
    "    epsilon=.1, \n",
    "    alpha=0.5, \n",
    "    discount=.9, \n",
    "    get_legal_actions=lambda s: range(env.action_space.n)\n",
    ")\n",
    "\n",
    "inverse_dynamics = InverseDynamics(\n",
    "    np.prod(env.observation_space.shape), \n",
    "    env.action_space.n, \n",
    "    16\n",
    ")\n",
    "\n",
    "train_with_reward(env, agent, inverse_dynamics, n_episodes=3000, \n",
    "                  update_reward_period=100, batch_size=100, n_iter=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAADnCAYAAAD/7faHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcZElEQVR4nO3dfZRdVZnn8e+vigSSEBIgvAcBMeJEmwZXBN9GsXkxIAtc3WonjgoOPTTO0CONjoPdvXAW3fQo0+3bwKhpoYGWF2kUO+OkiTQt2ijQBIhgiNFAA0mAQAwk4TWpqmf+OKfirUrVvbvqnnvPuff+Pmudxb3n7jpnnyL3Obv22fvZigjMzKy79ZVdATMzaz0HezOzHuBgb2bWAxzszcx6gIO9mVkP2K3sCpiZdYP3vmdG/HrzYFLZ+x58dXlELGxxlUZwsDczK8CmzYPcs3xuUtkpBz0yp8XV2YWDvZlZIYLBGCq7EuNysDczK0AAQ1R3kqqDvZlZQYZwy97MrKsFwQ5345iZdbcABt2NY2bW/dxnb2bW5QIYrHAWYQd7M7OCVLfH3sHezKwQQbjP3sys20XAjurGegd7M7NiiEFUdiXG5WBvZlaAAIbcsjcz635u2ZuZdblsUpWDvZlZVwtgR1R3PSgHezOzAgRisMKL/znYm5kVZCiq241T3dtQF5H0Tkk/lbRF0mZJP5H0FklnS7pzAsc5XFJI8k3arGKG++xTtjI4aLSYpL2A7wOfAG4CpgL/Hni1zHqZWdHEYIX77Ktbs+7xeoCIuCEiBiPi5Yj4AbAD+DrwNkkvSHoeQNL7JD0gaaukdZL+R82xfpz/9/n8Z96W/8x/lLRa0nOSlks6rG1XZ2bA8EpVfUlbGRzsW++XwKCkaySdKmlvgIhYDZwH3BURe0bE7Lz8i8DHgNnA+4BPSHp//tm78v/Ozn/mLklnAn8C/C6wH/AvwA2tvywzqxUhtkd/0lYGB/sWi4itwDvJbvx/AzwraamkA8Ypf0dEPBQRQxHxIFngfnedU5wH/M+IWB0RA8BfAse4dW/WfkMoaSuDg30b5IH47IiYC7wJOBj48lhlJR0v6YeSnpW0hSyYz6lz+MOAr0h6Pu8K2gwIOKTIazCz+rIHtH1JWxkc7NssIn4BXE0W9MfKpHE9sBQ4NCJmkfXrDzcFxiq/DvjDiJhds02LiJ8WX3szG1/2gDZlK4ODfYtJeoOkT0mam78/FFgM3A1sBOZKmlrzIzOBzRHxiqTjgA/XfPYs2foIr63Z93Xgs5LemB9/lqQPtu6KzGwsVX9A66GXrbcNOB64UNJs4HmyoZj/DXgFWAU8LWkoIuYA/xn4a0mXAz8iG645GyAiXpJ0KfATSVOAhRFxi6Q9gRvzfvotwG3A37fvEs0MYLDCk6oUFV4z0cysUxz5WzPiL2+Zn1R20bwV90XEghZXaQS37M3MCjD8gLaqHOzNzAoQqNLdOA72ZmYFKevhawoHezOzAkRQ6dw4bQ32U7V77MGMdp7SesgrvMj2eLW6f0e3mKQA5kXE2oKPewDZ6K5jgSUR8akij9/g3C8AR0fEo+0652QFYkdJqRBStDXY78EMjteJ7Tyl9ZB74vayq9AUSbcC/xoRF4/afybwDWBunhKj3c4FNgF7RQuH70m6A/hWRHxzeF9E7Nmq87VClR/QVrdmZr3nGuAjkkb/dfJR4LqSAj1kKTkebmWg7waBGIq0rQxNBXtJCyWtkbRW0kVFVcqsR30P2JdsvQMA8iyppwPXSjpO0l15HqSnJF0+avY1NT93h6Q/qHk/YqGcfGb3bfliOmskfWic41wNnAV8Jk+rfZKkqyX9RU2ZEyStr3n/mKRPS3owX7Dn25L2qPn8TEkr8zTej+Rx5NL8ui/Pz3N5XjYkvS5/PUvStXneqMcl/Zmkvtrrk/RXearvf5N0avqvvhhF5cZJja2Sfi//HTUcsz/pYC+pH7gCOBWYDyyWlDajwMx2EREvk82Y/ljN7g8Bv4iInwGDwB+TJcZ7G3Ai2YzrCZE0g2yW9fXA/sAi4P+M9f2NiLOB64DL8rTa/5R4mg8BC4EjgKOBs/NzHwdcSzaDfDZZ2u7HIuJPydJzn5+f5/wxjvm/gVlk6ULeTfZ7+njN58cDa8h+P5cBV47xV1LLBDAUfUlbPamxVdJM4JPAPSn1a6ZlfxywNiIejYjtwI3AmU0cz8yyrpwP1LSEP5bvIyLui4i7I2IgIh4j68evl/56PKeTBdi/zY/1APAdoMicSl+NiCcjYjPwf4Fj8v3nAFdFxG15Gu8NeXLAuvIAuAj4bERsy6//r8m6uIY9HhF/ExGDZL+zg4AxU4m3RtqShAnLEqbG1j8HvkCWdqWhZoL9IWQZF4etZ4y0upLOlbRC0oodXonPrK6IuJPsYej7JR1J9sW/HkDS6yV9X9LTkraSrV1QL/31eA4Djh9Oi52nxv4PwIGFXETm6ZrXLwHDD1oPBR6ZxPHmAFOAx2v2Pc7ImLPznBHxUv6ybQ94A9gR/UkbMGc4LubbuTWHahhbJb2ZLDPu/0utX8tH40TEEmAJwF7axw94zBq7lqxFfxSwPCI25vu/BjwALI6IbZIuAD4wzjFeBKbXvK8N5OuAH0XEyZOsX71jN7IOOHKcz+rFh01kS3keBjyc73sNsGEC526pCDXsoqmxabK5cfLnFF8k7xpL1UzLfgPZXXrYXCr0izfrYNcCJwH/ibwLJzcT2Aq8IOkNZIvYj2cl8LuSpucPOM+p+ez7wOslfVTSlHx7i6R/l1i/lcBpkvaRdCBwQeLPAVwJfFzSiZL6JB2SXwtkKb9fO9YP5V0zNwGXSpqZZ3i9EPjWBM7dcgXls28UW2eSrYdxh6THgLcCSxs9pG0m2N8LzJN0RD4iYBHZohtm1oS8P/qnwAxGfqc+Tba+wTayJS6/XecwXwK2kwXQa8gesg4ffxtwCtl39kmy7o8vALsnVvHvgJ8BjwE/aFCPESLiX8keqn6JLB33j8ha6wBfIXte8Zykr47x439E9lfFo8CdZN1bV6Weu9WyfPaFLEtYN7ZGxJaImBMRh0fE4WRrY5wRESvqHbSpFMeSTiNbXq+f7KHLpfXK76V9wpOqrFXuidvZGpt7dgatlevgN+4d59x4QlLZvzj6e3VTHI8VWyVdAqyIiKWjyt4BfLpRsG+qzz4ilgHLmjmGmVk3yIZeFtPWGCu2jp5ZXbP/hJRjOhGamVkBnBvHzKxHOMWxmVmXy1IcV/eRkYO9WZOm9k+Laf17NS7YV91AYI1tfXXjpojYr16ZspKcpXCwN2vStP69ePuBixuWi2mpIxutipb/8rLH632eZb2sbjdOdWtmViJndLWJytIl9CVtZXDL3myUmqyDJ5PlJblX0tKIeLj+T1pvc8verNM4o6tNSkEzaFvCLXuzXY2VdfD42gJ5lsJzAfbon9m+mllleTSOWReqzeY6a+oBzuZqAJXuxnGwN9uVM7rahA2vQVtVDvZmu9qZdZAsyC8iyzZpNq4ABtyyN+scETEg6XxgOb/JOriq5GpZB3A3jlmHmUhG19ixg4H1jXt5ht55TNK5p2zcmlTOKibcjWNm1vWGFy+pKgd7M7OCuGVvZtblily8pBUmHewlHUq2MPIBZNe5JCK+UlTFzMw6SSAGhrrzAe0A8KmIuF/STOA+Sbc5f4iZ9aqu7LOPiKeAp/LX2yStJptm7mBvZr0nurQbp5akw4FjgXvG+Ow3OUSYXsTpzMwqp2v77IdJ2hP4DnBBROwyQLg2h8he2sc5RMysa3VtsJc0hSzQXxcR3y2mSmZmnScQg934gFaSgCuB1RHxxeKqZNZZ1N9H/56N16Dte+BXScfbdsqbksrN/PmzSeWsfar8gLaZ29A7gI8CvyNpZb6dVlC9zMw6SuQPaFO2MjQzGudOqPBtzGySPIfEJiu6tc/erEt5DolNghOhmXUUzyGxyXLL3qxD1ZtDYlYrAgaHHOzNOk69OSQjJgtqRgm1syrq1tE4Zl2r0RySiFgSEQsiYsHUvj3aX0GrnCDrxknZGpG0UNIaSWslXTTG5+dJeigfBXmnpPmNjulgbzaK55DY5KQNu2z0EFdSP3AFcCowH1g8RjC/PiJ+KyKOAS4DGv47dbA325XnkNikRKRtDRwHrI2IRyNiO3AjcObI84zoVpxB9odFXe6zNxtlwnNI+vrRzD0bFhva9Oukw+1112NJ5Ta+77VJ5QD2//HG5LI2eRMYjTNH0oqa90vyPGKQjfxaV/PZeuD40QeQ9F+AC4GpwO80OqGDvZlZAbLROMmdJZsiYkFz54srgCskfRj4M+CseuXdjWNmVpCCunE2AIfWvJ+b7xvPjcD7Gx3Uwd7MrCAFjca5F5gn6QhJU4FFwNLaApLm1bx9H9Awy567cczMChCkDatseJyIAUnnA8uBfuCqiFgl6RJgRUQsBc6XdBKwA3iOBl044GBvZlaYolZniohlwLJR+y6uef3JiR7Twd7MrAgB4XQJZmbdz4nQzMx6QMJIm9I0PRpHUr+kByR9v4gKmZl1oiJz47RCES37TwKrgcaLcJqZdasAurUbR9JcsjGel5JN2zXrPUNDxEsvNyzW97rDkw4XTz6TVG7/u9LSLwCsO/PApHKH/sPTyce0XVW5G6fZlv2Xgc8AM8crMCLvN9ObPJ2ZWVWp0qNxJt1nL+l04JmIuK9eudq831PYfbKnM2s7P4+yCYvErQTNtOzfAZyRp37dA9hL0rci4iPFVM2sdH4eZemi2kMvJ92yj4jPRsTciDicLHfDPzvQW7eoeR71zbLrYh2kwi17J0IzG9uXyZ5HDY31oaRzJa2QtGJ7vNLWilmVKXFrv0KCfUTcERGnF3Ess7KlPI8asQatvAat5YYStxJ4Bq3Zrvw8yiau4uPs3Y1jNoqfR9lkFbR4SUu4ZW9mVpQunlRl1tUi4g7gjrqFJNgt4au0cVPSOQfe8JqkclMeS19E/KC70iY0/vqtBySV2/duL2A+pgp34zjYm5kVRG7Zm5l1uRBUOF2Cg72ZWVHcsjcz6wEO9mZmPcDB3sysy1V8UpWDvZlZQTwax8ysFzjYm5l1P7fszbpZn9D0hMyXSuvPnbIubabtS0fPTSoHMH1N2rq2+25OW03u5SP3TSo37ZH0dXK7gvvszcy6XIkLk6Rw1kuzMUiaLelmSb+QtFrS28quk3WAbl2pyl8I62JfAW6NiDcAv022Fq1ZXRpK28rQbMveXwjrOpJmAe8CrgSIiO0R8XyplbLOUFDLXtJCSWskrZV00RifXyjpYUkPSrpd0mGNjjnpYO8vhHWxI4Bngb+V9ICkb0qaUVtgxBq0gy+XU0urFEX6Vvc4Uj9wBXAqMB9YLGn+qGIPAAsi4mjgZuCyRvVrpmXf8AuRV3znl2IHrzZxOrO22Q14M/C1iDgWeBEY0boasQZt/7Qy6mhVFErb6jsOWBsRj0bEduBG4MwRp4n4YUS8lL+9G2g4NKuZYN/wC5FXaueXYgppw7rMSrYeWB8R9+Tvbyb7t25WXzHdOIcA62rer8/3jecc4B8bHbSZYO8vhHWliHgaWCfpqHzXicDDJVbJOsQEunHmDPd45Nu5kzqf9BFgAfC/GpWd9Dj7iHha0jpJR0XEGvyFsO7yR8B1kqYCjwIfL7k+VnUxoZE2myJiwTifbQAOrXk/N983gqSTgD8F3h0RDfvIm51U5S+EdaWIWEnWYmpMfcTuU4s7+dQpScWm//LZ5ENuffNBSeVmPpR2zGmPP59U7rm37J9Ubu9702b4Vl4xY+jvBeZJOoIsyC8CPlxbQNKxwDeAhRGR9MtrKthP6AthZtbtCgj2ETEg6XxgOdAPXBURqyRdAqyIiKVk3TZ7An+vLA3HExFxRr3jOl2CmVlBikqEFhHLgGWj9l1c8/qkiR7T6RLMzHqAW/ZmZkWpcCI0B3szsyJMbDRO2znYm5kVxS17M7PuJrxSlZlZb3CwNzPrcgkZLcvkYG/WrD4R0xsn+YvENWj7tg8klZtIXNnz0W1px0ycCTywd1qmz9SZsdvnzk4qN3X980nlSuMHtGZm3a/KLXtPqjIbg6Q/lrRK0s8l3SBpj7LrZB2gW9egNetGkg4B/ivZSkBvIstPsqjcWlnlpQb6koK9u3HMxrYbME3SDmA68GTJ9bEO4G4csw4SERuAvwKeAJ4CtkTED2rLjFiDduClsQ5jvajCLXsHe7NRJO1NtubnEcDBwIx8RaCdRqxBu9v0MqppFaShtK0MDvZmuzoJ+LeIeDYidgDfBd5ecp2s6ireZ+9gb7arJ4C3SpqubGWIE4HVJdfJKk4T2MrQVLD38DTrRhFxD3AzcD/wENn3ZEmplbLOUOGW/aRH49QMT5sfES9LuolseNrVBdXNrDQR8Tngc0ll+8TgtMbrxsZuiW2rF9KKxczGs3aHaSCto3go4ToApmzcmlRuyzH7JZWbtTJt7duhPdNm7gL0vfByctmiVHk0TrNDLz08zcxsWIWD/aS7cVKGp8HIIWo7eHXyNTUzq7Lo0tE4KcPTYOQQtSmk/9lpZtZxKtxn38wDWg9PMzOroUjbytBMsPfwNDOzWhVu2U/6AW1E3CNpeHjaAPAAHp5mZj2sa0fjTGR4mplZVwu8eImZWbfzguNmZr3Cwd7MrPspqhvtHezNmhR9YnB6469S3/a0Dt1XDkxLmbzby4NJ5QCGpqYNvNNAYrDab2ZSsVk/25RUbnDvGUnl+p97MakcwKuv2Tup3O5PPJd8zLpKHGmTwsHezKwgVe6zd4pj61mSrpL0jKSf1+zbR9Jtkn6V/zeteWhGcekSJC2UtEbSWkkXjfH5uyTdL2lA0gdS6uZgb73samDhqH0XAbdHxDzg9vy9WZoCJlVJ6geuAE4F5gOLJc0fVewJ4Gzg+tSqOdhbz4qIHwObR+0+E7gmf30N8P521sk6WGKqhISunuOAtRHxaERsB24k+3f5m1NFPBYRDzKBkf0O9mYjHRART+WvnwYOGKvQiGyuO9IfGlqXS2/Zzxn+95Nv59Yc5RBgXc379fm+pvgBrdk4IiKksdthEbGEPD3IzL3mVvixnLXLBCdVbYqIBa2rza4c7M1G2ijpoIh4StJBwDNlV8g6h4YKue9vAA6teT8339cUd+OYjbQUOCt/fRbwDyXWxTpJahdO4/vBvcA8SUdImkq23OvSZqvnYG89S9INwF3AUZLWSzoH+DxwsqRfka3Z8Pky62idpYihlxExAJwPLCdLG39TRKySdImkMwAkvUXSeuCDwDckrWpUN3fjWM+KiMXjfHTihI7TDztm9jcsp8HGZbIDphV7de+0xcEB+lJnxiYuJjc0RUnldszcJ6nctCe2pB3v4FlJ5SB9ZuyyH303qVz/QQmFCnp6ExHLgGWj9l1c8/pesu6dZA72ZmYFqfIMWgd7M7MiBFDhRGgN++w9pdzMLE1R6RJaIeUB7dV4SrmZWV3D4+w7dsFxTyk3M0sQkb6VYLJ99klTyiGbVg6cC7AHaXm6zcw6UZUf0DY9zj4i6k4TiIglEbEgIhZMSR3XZWbWiYqZVNUSkw32G/Op5HhKuZlZpqP77MfhKeVmZrUCGIy0rQQN++zzKeUnkKXkXA98jmwK+U359PLHgQ+1spJmVfbi8xs2/eSWzzw+xkdzgLRFWKutW64DxriWpJmxmcMaFahyn33DYF/UlHKzbhUR+421X9KKdqexbYVuuQ5ow7VUeFKVZ9B2meVPrpzwz7z34GMKr4dZL+rolr2ZmSUocaRNCgd7s9ZZUnYFCtIt1wEtvBYBKunhawoHe7MWyZcu7Hjdch3Q+muR++zNzLqcu3HMzHpBeXlvUnhZQrOCSVooaY2ktZI6OiOspMckPSRppaQVZddnIspIz96NM2jNbAyS+oErgFOB+cBiSfPLrVXT3hMRx3TgWPuraXd69gpnvXSwNyvWccDaiHg0IrYDN5KlBLc2a3t69shG46RsZXCwNyvWIcC6mvfr832dKoAfSLovT1fe6ZLTs09KhbNe+gGtmdXzzojYIGl/4DZJv8hbzB0vIkIqtge9ykMv3bI3K9YG4NCa93PzfR0pIjbk/30GuIWsm6qTtTY9u/vszXrGvcA8SUdImgosIksJ3nEkzZA0c/g1cArw8/o/VXmtS88ewFDiVoK2duNs47lN/xQ3VzEVbNecfwLpWmvOvbYrrp2EFLStFhEDks4HlgP9wFURsarkak3WAcAtkiCLFddHxK3lVildu9Ozi6h0N05bg31VU8H28vl7+dpbJSKWAcvKrkezIuJR4LfLrsdklZKefaikZnsCP6A1MyvCcDdORTnYm5kVxN04jZWdVa+Xz9/L125WLAf7+spOodrL5+/lazcrlhOhmZl1vwAGI21roFEyPUm7S/p2/vk9kg5vdEwHezOzgigiaat7jLRkeucAz0XE64AvAV9oVLe2BvtW3K0mcO5DJf1Q0sOSVkn65BhlTpC0JU/nulLSxQWev26qWGW+ml/7g5LeXOC5j6q5ppWStkq6YFSZQq+9mfSyks7Ky/xK0lljlTGrpGJm0KYk06tN6HYzcKLyCRHjaVuffc3d6mSy5FD3SloaEQ/XFNt5t5K0iOxu9fsFVWEA+FRE3J/PCrxP0m2jzg/wLxFxekHnHO09ETHeBKJTgXn5djzwtfy/TYuINcAxsPP/wwayqe+jFXntVwOXA9fW7BtOL/v5/GZ/EfDfa39I0j5kk18WkP1hfF/+7+S5gupl1hoBDCX32c8Z1ehbUvP8aqxkeqNjwc4y+US+LcC+1Jmg2M6WfUvuVqki4qmIuD9/vQ1YTbWyEZ4JXBuZu4HZwzk8CnYi8EhEjDWTuTBNpJd9L3BbRGzOA/xt7JqT3KyCElv1Wct+U0QsqNlaPlChncE+JfXriLsVMHy3KlTePXQscM8YH79N0s8k/aOkNxZ42kapYtuVGncRcMM4n7Xq2oelpJftthTB1kuK6cZJSaa3s4yk3YBZwK/rHbQSQy/bSdKewHeACyJi66iP7wcOi4gXJJ0GfI+sW6UIpaeKzRNznQF8doyPW3ntu2hFelmzUgUwWMgU2p3J9MiC+iLgw6PKDCd0uwv4APDPEfXvIu1s2bfkbjURkqaQBfrrIuK7oz+PiK0R8UL+ehkwRdKcIs6dkCq2HalxTwXuj4iNY9SvZddeIyW9bFelCLZeEhBDaVu9o2S9GsPJ9FYDN0XEKkmXSDojL3YlsK+ktcCFJCyv2M5gn5L6tTb9aNLdKlXe938lsDoivjhOmQOHnxFIOo7s99P0zUZpqWKXAh/LR+W8FdhS0+VRlMWM04XTqmsfJSW97HLgFEl756N1Tsn3mVVfQfnsI2JZRLw+Io6MiEvzfRdHxNL89SsR8cGIeF1EHJcnraurbd0446V+lXQJsCK/iCuBv8vvVpvJbghFeQfwUeAhSSvzfX8CvCav39fJbjCfkDQAvAwsKuhmM2aqWEnn1Zx7GXAasBZ4Cfh4AefdKb/JnAz8Yc2+2vMXeu2aQHpZSQuA8yLiDyJis6Q/J2scAFwSEaMf9JpVz8RG47SdCmo4m5n1tFlTD4i3H5DWPr11/Vfva3dq7557QGtm1jIVbjw72JuZFSECBgfLrsW4HOzNzIrilr2ZWQ9wsDcz63ZR6dE4DvZmZkUIiAYTpsrkYG9mVpRi0iW0hIO9mVkRImDIwd7MrPv5Aa2ZWfcLt+zNzLpdWpKzsjjYm5kVoeKJ0BzszcwKEEA4XYKZWZeLaLgwSZkc7M3MChLuxjEz6wEVbtl78RIzswJIuhVIXbd5U0QsbGV9RnOwNzPrAe1ccNzMzEriYG9m1gMc7M3MeoCDvZlZD3CwNzPrAf8fbJZMDI5qc2MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_agent(agent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3.3 Intrinsic Curiosity Module algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Embedder(nn.Module):\n",
    "    def __init__(self, states_size, embedding_size, hidden_size):\n",
    "        super().__init__()\n",
    "        self.module = MLP(\n",
    "            states_size,\n",
    "            hidden_size,\n",
    "            embedding_size\n",
    "        )\n",
    "        \n",
    "    def forward(self, s):\n",
    "        return self.module(s)\n",
    "        \n",
    "class ICMModule(BaseIntrinsicRewardModule):\n",
    "    def __init__(self, states_size, n_actions, hidden_size, embedding_size):\n",
    "        super().__init__()\n",
    "        # <YOUR CODE>\n",
    "    \n",
    "    def get_intrinsic_reward(self, state, action, next_state):\n",
    "        with torch.no_grad():            \n",
    "            # <YOUR CODE>\n",
    "\n",
    "    def get_loss(self, state_batch, action_batch, next_state_batch):\n",
    "        # <YOUR CODE>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "agent = QLearningAgent(\n",
    "    epsilon=.1, \n",
    "    alpha=0.5, \n",
    "    discount=1, \n",
    "    get_legal_actions=lambda s: range(env.action_space.n)\n",
    ")\n",
    "\n",
    "icm = ICMModule(\n",
    "    states_size=np.prod(env.observation_space.shape), \n",
    "    n_actions=env.action_space.n, \n",
    "    hidden_size=16, embedding_size=10\n",
    ")\n",
    "\n",
    "train_with_reward(env, agent, icm, n_episodes=3000, update_reward_period=100, batch_size=100, n_iter=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_agent(agent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HW 2.1: Random network distillation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implement algorithm from [this](https://arxiv.org/abs/1810.12894) paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RandomNetworkDistilationModule(BaseIntrinsicRewardModule):\n",
    "    # <YOUR CODE>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "agent = QLearningAgent(\n",
    "    epsilon=.1, \n",
    "    alpha=0.5, \n",
    "    discount=.9, \n",
    "    get_legal_actions=lambda s: range(env.action_space.n)\n",
    ")\n",
    "\n",
    "rnd = RandomNetworkDistilationModule(\n",
    "    np.prod(env.observation_space.shape), \n",
    "    np.prod(env.observation_space.shape), \n",
    "    16\n",
    ")\n",
    "\n",
    "train_with_reward(env, agent, rnd, n_episodes=2000, update_reward_period=100, batch_size=100, n_iter=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_agent(agent)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
